Overview

Dataset statistics

Number of variables12
Number of observations24
Missing cells23
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory107.5 B

Variable types

Numeric6
Categorical2
Text4

Dataset

Description경상남도내 수영장 현황입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3079741

Alerts

시도 has constant value ""Constant
실내외 구분 has constant value ""Constant
연번 is highly overall correlated with 건축면적(제곱미터)High correlation
부지면적(제곱미터) is highly overall correlated with 관람석 수용인원(명)High correlation
건축면적(제곱미터) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 건축면적(제곱미터)High correlation
관람석 수용인원(명) is highly overall correlated with 부지면적(제곱미터)High correlation
부지면적(제곱미터) has 6 (25.0%) missing valuesMissing
건축면적(제곱미터) has 1 (4.2%) missing valuesMissing
연면적(제곱미터) has 1 (4.2%) missing valuesMissing
관람석 수용인원(명) has 15 (62.5%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:46:18.546388
Analysis finished2023-12-10 22:46:22.601455
Duration4.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T07:46:22.678878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2023-12-11T07:46:22.863074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
경상남도
24 

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 24
100.0%

Length

2023-12-11T07:46:22.976793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:46:23.062094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 24
100.0%

시군구
Text

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T07:46:23.181843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
Distinct characters25
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 (%)41.7%

Sample

1st row창원시
2nd row창원시
3rd row창원시
4th row창원시
5th row창원시
ValueCountFrequency (%)
창원시 10
41.7%
통영시 2
 
8.3%
남해군 2
 
8.3%
진주시 1
 
4.2%
사천시 1
 
4.2%
김해시 1
 
4.2%
밀양시 1
 
4.2%
거제시 1
 
4.2%
의령군 1
 
4.2%
고성군 1
 
4.2%
Other values (3) 3
 
12.5%
2023-12-11T07:46:23.460633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 17
23.6%
창 10
13.9%
원 10
13.9%
군 7
9.7%
해 3
 
4.2%
통 2
 
2.8%
영 2
 
2.8%
남 2
 
2.8%
양 2
 
2.8%
천 2
 
2.8%
Other values (15) 15
20.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 17
23.6%
창 10
13.9%
원 10
13.9%
군 7
9.7%
해 3
 
4.2%
통 2
 
2.8%
영 2
 
2.8%
남 2
 
2.8%
양 2
 
2.8%
천 2
 
2.8%
Other values (15) 15
20.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 17
23.6%
창 10
13.9%
원 10
13.9%
군 7
9.7%
해 3
 
4.2%
통 2
 
2.8%
영 2
 
2.8%
남 2
 
2.8%
양 2
 
2.8%
천 2
 
2.8%
Other values (15) 15
20.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 17
23.6%
창 10
13.9%
원 10
13.9%
군 7
9.7%
해 3
 
4.2%
통 2
 
2.8%
영 2
 
2.8%
남 2
 
2.8%
양 2
 
2.8%
천 2
 
2.8%
Other values (15) 15
20.8%

시설명
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T07:46:23.659310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length11.583333
Min length8

Characters and Unicode

Total characters278
Distinct characters76
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

Unique24 ?
Unique (%)100.0%

Sample

1st row창원 실내수영장
2nd row늘푸른전당 수영장
3rd row시민생활체육관 수영장
4th row서부스포츠센터 수영장
5th row올림픽기념국민생활관 체육관 부설수영장
ValueCountFrequency (%)
수영장 11
23.4%
실내수영장 9
19.1%
창원 1
 
2.1%
통영 1
 
2.1%
함양국민체육센터(수영장 1
 
2.1%
산청문화예술회관 1
 
2.1%
남해국민체육센터 1
 
2.1%
남해스포츠파크 1
 
2.1%
고성군문화체육센터 1
 
2.1%
의령국민체육센터수영장 1
 
2.1%
Other values (19) 19
40.4%
2023-12-11T07:46:23.991073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
영 26
 
9.4%
수 24
 
8.6%
장 24
 
8.6%
23
 
8.3%
내 12
 
4.3%
센 10
 
3.6%
실 10
 
3.6%
터 10
 
3.6%
육 9
 
3.2%
체 9
 
3.2%
Other values (66) 121
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 253
91.0%
Space Separator 23
 
8.3%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 26
 
10.3%
수 24
 
9.5%
장 24
 
9.5%
내 12
 
4.7%
센 10
 
4.0%
실 10
 
4.0%
터 10
 
4.0%
육 9
 
3.6%
체 9
 
3.6%
민 8
 
3.2%
Other values (63) 111
43.9%
Space Separator
ValueCountFrequency (%)
23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 253
91.0%
Common 25
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 26
 
10.3%
수 24
 
9.5%
장 24
 
9.5%
내 12
 
4.7%
센 10
 
4.0%
실 10
 
4.0%
터 10
 
4.0%
육 9
 
3.6%
체 9
 
3.6%
민 8
 
3.2%
Other values (63) 111
43.9%
Common
ValueCountFrequency (%)
23
92.0%
( 1
 
4.0%
) 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 253
91.0%
ASCII 25
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
영 26
 
10.3%
수 24
 
9.5%
장 24
 
9.5%
내 12
 
4.7%
센 10
 
4.0%
실 10
 
4.0%
터 10
 
4.0%
육 9
 
3.6%
체 9
 
3.6%
민 8
 
3.2%
Other values (63) 111
43.9%
ASCII
ValueCountFrequency (%)
23
92.0%
( 1
 
4.0%
) 1
 
4.0%

소유기관
Text

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T07:46:24.128299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
Distinct characters25
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 (%)41.7%

Sample

1st row창원시
2nd row창원시
3rd row창원시
4th row창원시
5th row창원시
ValueCountFrequency (%)
창원시 10
41.7%
통영시 2
 
8.3%
남해군 2
 
8.3%
진주시 1
 
4.2%
사천시 1
 
4.2%
김해시 1
 
4.2%
밀양시 1
 
4.2%
거제시 1
 
4.2%
의령군 1
 
4.2%
고성군 1
 
4.2%
Other values (3) 3
 
12.5%
2023-12-11T07:46:24.407607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 17
23.6%
창 10
13.9%
원 10
13.9%
군 7
9.7%
해 3
 
4.2%
통 2
 
2.8%
영 2
 
2.8%
남 2
 
2.8%
양 2
 
2.8%
천 2
 
2.8%
Other values (15) 15
20.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 17
23.6%
창 10
13.9%
원 10
13.9%
군 7
9.7%
해 3
 
4.2%
통 2
 
2.8%
영 2
 
2.8%
남 2
 
2.8%
양 2
 
2.8%
천 2
 
2.8%
Other values (15) 15
20.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 17
23.6%
창 10
13.9%
원 10
13.9%
군 7
9.7%
해 3
 
4.2%
통 2
 
2.8%
영 2
 
2.8%
남 2
 
2.8%
양 2
 
2.8%
천 2
 
2.8%
Other values (15) 15
20.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 17
23.6%
창 10
13.9%
원 10
13.9%
군 7
9.7%
해 3
 
4.2%
통 2
 
2.8%
영 2
 
2.8%
남 2
 
2.8%
양 2
 
2.8%
천 2
 
2.8%
Other values (15) 15
20.8%

관리주체
Text

Distinct15
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T07:46:24.575941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.7916667
Min length3

Characters and Unicode

Total characters187
Distinct characters54
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

Unique12 ?
Unique (%)50.0%

Sample

1st row시설관리공단
2nd row시설관리공단
3rd row시설관리공단
4th row시설관리공단
5th row시설관리공단
ValueCountFrequency (%)
시설관리공단 8
26.7%
위탁 3
 
10.0%
체육시설사업소 3
 
10.0%
통영관광개발공사 2
 
6.7%
민간위탁 2
 
6.7%
함양군 1
 
3.3%
산청군체육회 1
 
3.3%
고성군 1
 
3.3%
의령군 1
 
3.3%
재)거제시문화예술재단 1
 
3.3%
Other values (7) 7
23.3%
2023-12-11T07:46:24.890690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 16
 
8.6%
15
 
8.0%
설 12
 
6.4%
관 11
 
5.9%
공 11
 
5.9%
단 11
 
5.9%
리 9
 
4.8%
) 6
 
3.2%
( 6
 
3.2%
사 6
 
3.2%
Other values (44) 84
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 160
85.6%
Space Separator 15
 
8.0%
Close Punctuation 6
 
3.2%
Open Punctuation 6
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 16
 
10.0%
설 12
 
7.5%
관 11
 
6.9%
공 11
 
6.9%
단 11
 
6.9%
리 9
 
5.6%
사 6
 
3.8%
탁 5
 
3.1%
체 5
 
3.1%
위 5
 
3.1%
Other values (41) 69
43.1%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 160
85.6%
Common 27
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 16
 
10.0%
설 12
 
7.5%
관 11
 
6.9%
공 11
 
6.9%
단 11
 
6.9%
리 9
 
5.6%
사 6
 
3.8%
탁 5
 
3.1%
체 5
 
3.1%
위 5
 
3.1%
Other values (41) 69
43.1%
Common
ValueCountFrequency (%)
15
55.6%
) 6
 
22.2%
( 6
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 160
85.6%
ASCII 27
 
14.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 16
 
10.0%
설 12
 
7.5%
관 11
 
6.9%
공 11
 
6.9%
단 11
 
6.9%
리 9
 
5.6%
사 6
 
3.8%
탁 5
 
3.1%
체 5
 
3.1%
위 5
 
3.1%
Other values (41) 69
43.1%
ASCII
ValueCountFrequency (%)
15
55.6%
) 6
 
22.2%
( 6
 
22.2%

부지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)88.9%
Missing6
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean20254.333
Minimum5832
Maximum52765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T07:46:25.024554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5832
5-th percentile6241.7
Q18216.5
median19020.5
Q329151.25
95-th percentile41596.85
Maximum52765
Range46933
Interquartile range (IQR)20934.75

Descriptive statistics

Standard deviation13244.178
Coefficient of variation (CV)0.65389356
Kurtosis0.44175549
Mean20254.333
Median Absolute Deviation (MAD)11129
Skewness0.89263528
Sum364578
Variance1.7540825 × 108
MonotonicityNot monotonic
2023-12-11T07:46:25.129363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9916 2
 
8.3%
20476 2
 
8.3%
31326 1
 
4.2%
7650 1
 
4.2%
5832 1
 
4.2%
15478 1
 
4.2%
52765 1
 
4.2%
24119 1
 
4.2%
39626 1
 
4.2%
6934 1
 
4.2%
Other values (6) 6
25.0%
(Missing) 6
25.0%
ValueCountFrequency (%)
5832 1
4.2%
6314 1
4.2%
6934 1
4.2%
7616 1
4.2%
7650 1
4.2%
9916 2
8.3%
15478 1
4.2%
17565 1
4.2%
20476 2
8.3%
24119 1
4.2%
ValueCountFrequency (%)
52765 1
4.2%
39626 1
4.2%
31780 1
4.2%
31326 1
4.2%
29908 1
4.2%
26881 1
4.2%
24119 1
4.2%
20476 2
8.3%
17565 1
4.2%
15478 1
4.2%

건축면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean2861.8261
Minimum934
Maximum8206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T07:46:25.232276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum934
5-th percentile1284.3
Q11755.5
median2394
Q33257
95-th percentile6185.9
Maximum8206
Range7272
Interquartile range (IQR)1501.5

Descriptive statistics

Standard deviation1773.6367
Coefficient of variation (CV)0.61975698
Kurtosis2.961328
Mean2861.8261
Median Absolute Deviation (MAD)735
Skewness1.7063863
Sum65822
Variance3145787.2
MonotonicityNot monotonic
2023-12-11T07:46:25.362219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8206 1
 
4.2%
1287 1
 
4.2%
3077 1
 
4.2%
1771 1
 
4.2%
1772 1
 
4.2%
2197 1
 
4.2%
934 1
 
4.2%
1836 1
 
4.2%
1467 1
 
4.2%
1354 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
934 1
4.2%
1284 1
4.2%
1287 1
4.2%
1354 1
4.2%
1467 1
4.2%
1740 1
4.2%
1771 1
4.2%
1772 1
4.2%
1836 1
4.2%
2197 1
4.2%
ValueCountFrequency (%)
8206 1
4.2%
6226 1
4.2%
5825 1
4.2%
3889 1
4.2%
3424 1
4.2%
3385 1
4.2%
3129 1
4.2%
3077 1
4.2%
2995 1
4.2%
2870 1
4.2%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4810.913
Minimum785
Maximum14329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T07:46:25.481996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum785
5-th percentile963.2
Q11945
median3185
Q36009.5
95-th percentile13586.9
Maximum14329
Range13544
Interquartile range (IQR)4064.5

Descriptive statistics

Standard deviation4201.9407
Coefficient of variation (CV)0.87341854
Kurtosis0.47235254
Mean4810.913
Median Absolute Deviation (MAD)1831
Skewness1.2953419
Sum110651
Variance17656305
MonotonicityNot monotonic
2023-12-11T07:46:25.608806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
14329 1
 
4.2%
1287 1
 
4.2%
6796 1
 
4.2%
2769 1
 
4.2%
2762 1
 
4.2%
4213 1
 
4.2%
934 1
 
4.2%
2580 1
 
4.2%
785 1
 
4.2%
1354 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
785 1
4.2%
934 1
4.2%
1226 1
4.2%
1287 1
4.2%
1354 1
4.2%
1532 1
4.2%
2358 1
4.2%
2580 1
4.2%
2762 1
4.2%
2769 1
4.2%
ValueCountFrequency (%)
14329 1
4.2%
13774 1
4.2%
11903 1
4.2%
11515 1
4.2%
7915 1
4.2%
6796 1
4.2%
5223 1
4.2%
4213 1
4.2%
3980 1
4.2%
3854 1
4.2%

실내외 구분
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
실내
24 

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 24
100.0%

Length

2023-12-11T07:46:25.735934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:46:25.830253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실내 24
100.0%

관람석 수용인원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)77.8%
Missing15
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean776.55556
Minimum91
Maximum3078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T07:46:25.907245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile94.6
Q1100
median400
Q3500
95-th percentile2614.8
Maximum3078
Range2987
Interquartile range (IQR)400

Descriptive statistics

Standard deviation1031.4685
Coefficient of variation (CV)1.3282611
Kurtosis2.6429225
Mean776.55556
Median Absolute Deviation (MAD)300
Skewness1.8363933
Sum6989
Variance1063927.3
MonotonicityNot monotonic
2023-12-11T07:46:25.999144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
500 2
 
8.3%
100 2
 
8.3%
3078 1
 
4.2%
91 1
 
4.2%
400 1
 
4.2%
300 1
 
4.2%
1920 1
 
4.2%
(Missing) 15
62.5%
ValueCountFrequency (%)
91 1
4.2%
100 2
8.3%
300 1
4.2%
400 1
4.2%
500 2
8.3%
1920 1
4.2%
3078 1
4.2%
ValueCountFrequency (%)
3078 1
4.2%
1920 1
4.2%
500 2
8.3%
400 1
4.2%
300 1
4.2%
100 2
8.3%
91 1
4.2%

준공연도
Real number (ℝ)

Distinct17
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.0833
Minimum1905
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T07:46:26.134941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1905
5-th percentile1989
Q11997
median2003
Q32008.25
95-th percentile2014.85
Maximum2016
Range111
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation21.504128
Coefficient of variation (CV)0.010756994
Kurtosis17.330782
Mean1999.0833
Median Absolute Deviation (MAD)6
Skewness-3.8939389
Sum47978
Variance462.42754
MonotonicityNot monotonic
2023-12-11T07:46:26.299879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2003 4
16.7%
1997 2
 
8.3%
1989 2
 
8.3%
2008 2
 
8.3%
2011 2
 
8.3%
2007 1
 
4.2%
1998 1
 
4.2%
2014 1
 
4.2%
2006 1
 
4.2%
2015 1
 
4.2%
Other values (7) 7
29.2%
ValueCountFrequency (%)
1905 1
 
4.2%
1989 2
8.3%
1992 1
 
4.2%
1993 1
 
4.2%
1997 2
8.3%
1998 1
 
4.2%
1999 1
 
4.2%
2002 1
 
4.2%
2003 4
16.7%
2006 1
 
4.2%
ValueCountFrequency (%)
2016 1
 
4.2%
2015 1
 
4.2%
2014 1
 
4.2%
2011 2
8.3%
2009 1
 
4.2%
2008 2
8.3%
2007 1
 
4.2%
2006 1
 
4.2%
2003 4
16.7%
2002 1
 
4.2%

Interactions

2023-12-11T07:46:21.636384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:18.893164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.314004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.808585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.296529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.795376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:21.737600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:18.952285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.417524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.889287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.368234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.881066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:21.830647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.013983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.483476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.977896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.438545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.963068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:21.934864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.084063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.569898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.053602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.521728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:21.358885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:22.012422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.163677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.654087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.135410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.618754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:21.446093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:22.094587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.247294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:19.735679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.213550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:20.710276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:21.544080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:46:26.391944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구시설명소유기관관리주체부지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)관람석 수용인원(명)준공연도
연번1.0000.7821.0000.7820.8160.3970.0000.0000.8910.000
시군구0.7821.0001.0001.0001.0000.4680.0000.0000.0000.000
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소유기관0.7821.0001.0001.0001.0000.4680.0000.0000.0000.000
관리주체0.8161.0001.0001.0001.0000.0000.0000.0000.0000.000
부지면적(제곱미터)0.3970.4681.0000.4680.0001.0000.3770.3980.0000.122
건축면적(제곱미터)0.0000.0001.0000.0000.0000.3771.0000.7000.7250.348
연면적(제곱미터)0.0000.0001.0000.0000.0000.3980.7001.0000.0000.000
관람석 수용인원(명)0.8910.0001.0000.0000.0000.0000.7250.0001.0000.000
준공연도0.0000.0001.0000.0000.0000.1220.3480.0000.0001.000
2023-12-11T07:46:26.503865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)관람석 수용인원(명)준공연도
연번1.000-0.284-0.522-0.347-0.2860.338
부지면적(제곱미터)-0.2841.000-0.025-0.0520.5510.030
건축면적(제곱미터)-0.522-0.0251.0000.6880.067-0.244
연면적(제곱미터)-0.347-0.0520.6881.000-0.0340.266
관람석 수용인원(명)-0.2860.5510.067-0.0341.000-0.359
준공연도0.3380.030-0.2440.266-0.3591.000

Missing values

2023-12-11T07:46:22.218917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:46:22.396195image/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.
2023-12-11T07:46:22.525123image/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)실내외 구분관람석 수용인원(명)준공연도
01경상남도창원시창원 실내수영장창원시시설관리공단39626820614329실내30781997
12경상남도창원시늘푸른전당 수영장창원시시설관리공단3178012871287실내<NA>1997
23경상남도창원시시민생활체육관 수영장창원시시설관리공단761633853385실내<NA>1992
34경상남도창원시서부스포츠센터 수영장창원시시설관리공단9916388911903실내<NA>2009
45경상남도창원시올림픽기념국민생활관 체육관 부설수영장창원시시설관리공단2047628707915실내<NA>1989
56경상남도창원시우리누리 청소년문화센터 실내수영장창원시시설관리공단29908582513774실내912008
67경상남도창원시마산실내체육관내 수영장창원시시설관리공단2047662261532실내<NA>1989
78경상남도창원시진동종합복지관내 실내수영장창원시시설관리공단2688131295223실내5002011
89경상남도창원시곰두리국민체육센터 수영장창원시경남지체장애인협회 창원지회631422221226실내<NA>1905
910경상남도창원시성산스포츠센터수영장창원시창원시시설관리공단9916253811515실내<NA>2016
연번시도시군구시설명소유기관관리주체부지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)실내외 구분관람석 수용인원(명)준공연도
1415경상남도김해시시민스포츠센터 수영장김해시김해문화재단3132617402358실내19202003
1516경상남도밀양시미리벌관 수영장밀양시체육시설사업소<NA><NA><NA>실내<NA>2003
1617경상남도거제시거제시문화예술회관 수영장거제시위탁 ((재)거제시문화예술재단)2411913541354실내<NA>2003
1718경상남도의령군의령국민체육센터수영장의령군의령군527651467785실내<NA>2007
1819경상남도고성군고성군문화체육센터 수영장고성군고성군<NA>18362580실내<NA>2003
1920경상남도남해군남해스포츠파크 실내수영장남해군체육시설사업소15478934934실내<NA>2002
2021경상남도남해군남해국민체육센터 실내수영장남해군체육시설사업소583221974213실내<NA>2015
2122경상남도산청군산청문화예술회관 수영장산청군위탁 (산청군체육회)765017722762실내<NA>2006
2223경상남도함양군함양국민체육센터(수영장)함양군함양군<NA>17712769실내1002014
2324경상남도합천군합천 실내수영장합천군위탁 (개인)<NA>30776796실내1001998