Overview

Dataset statistics

Number of variables13
Number of observations85
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory114.6 B

Variable types

Numeric7
Text2
Categorical3
DateTime1

Dataset

Description사상구 관내 소규모 동네 체육시설(동네,약수터, 학교내 체육시설)운영 현황:설치유형, 체력단련시설(운동기구) 시설종류, 체력단련시설(운동기구) 설치대수, 간이운동시설(테니스장 등) 시설 종류, 간이운동시설(테니스장 등) 설치대수, 최초설치연도, 면적
Author부산광역시 사상구
URLhttps://www.data.go.kr/data/3078733/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 소재지 경도 and 2 other fieldsHigh correlation
소재지 경도 is highly overall correlated with 연번High correlation
체력단련시설(운동기구) 시설종류(종) is highly overall correlated with 체력단련시설(운동기구) 설치대수(점) and 2 other fieldsHigh correlation
체력단련시설(운동기구) 설치대수(점) is highly overall correlated with 체력단련시설(운동기구) 시설종류(종) and 2 other fieldsHigh correlation
최초설치연도 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
면적(제곱미터) is highly overall correlated with 연번 and 5 other fieldsHigh correlation
설치유형 is highly overall correlated with 최초설치연도High correlation
간이운동시설(테니스장 등) 시설종류(종) is highly overall correlated with 면적(제곱미터)High correlation
간이운동시설(테니스장 등) 설치대수(점) is highly overall correlated with 면적(제곱미터)High correlation
간이운동시설(테니스장 등) 시설종류(종) is highly imbalanced (63.5%)Imbalance
간이운동시설(테니스장 등) 설치대수(점) is highly imbalanced (63.5%)Imbalance
최초설치연도 has 1 (1.2%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:44:09.187713
Analysis finished2024-04-06 08:44:25.240741
Duration16.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-04-06T17:44:25.445839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q122
median43
Q364
95-th percentile80.8
Maximum85
Range84
Interquartile range (IQR)42

Descriptive statistics

Standard deviation24.681302
Coefficient of variation (CV)0.57398377
Kurtosis-1.2
Mean43
Median Absolute Deviation (MAD)21
Skewness0
Sum3655
Variance609.16667
MonotonicityStrictly increasing
2024-04-06T17:44:25.818618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
55 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
Other values (75) 75
88.2%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
85 1
1.2%
84 1
1.2%
83 1
1.2%
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%

시설명
Text

UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
2024-04-06T17:44:26.450665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length12.658824
Min length7

Characters and Unicode

Total characters1076
Distinct characters181
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)100.0%

Sample

1st row낙동제방(금강화학) 체육시설
2nd row낙동제방(서부주유소) 체육시설
3rd row낙동제방(삼락동사) 체육시설
4th row낙동제방(삼락생태공원) 체육시설
5th row모라벽산＠뒤 체육시설
ValueCountFrequency (%)
체육시설 75
39.7%
동네체육시설 4
 
2.1%
건너편 3
 
1.6%
옆 3
 
1.6%
승학약수터 2
 
1.1%
새뜰마을 2
 
1.1%
온골마을 2
 
1.1%
모라3동 2
 
1.1%
밑 2
 
1.1%
학장제방(학장교옆 1
 
0.5%
Other values (93) 93
49.2%
2024-04-06T17:44:27.440222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
104
 
9.7%
시 82
 
7.6%
체 81
 
7.5%
육 81
 
7.5%
설 80
 
7.4%
수 33
 
3.1%
터 31
 
2.9%
동 30
 
2.8%
약 26
 
2.4%
학 24
 
2.2%
Other values (171) 504
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 916
85.1%
Space Separator 104
 
9.7%
Close Punctuation 17
 
1.6%
Open Punctuation 17
 
1.6%
Decimal Number 9
 
0.8%
Uppercase Letter 7
 
0.7%
Other Punctuation 6
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 82
 
9.0%
체 81
 
8.8%
육 81
 
8.8%
설 80
 
8.7%
수 33
 
3.6%
터 31
 
3.4%
동 30
 
3.3%
약 26
 
2.8%
학 24
 
2.6%
방 14
 
1.5%
Other values (155) 434
47.4%
Uppercase Letter
ValueCountFrequency (%)
N 1
14.3%
H 1
14.3%
C 1
14.3%
I 1
14.3%
L 1
14.3%
P 1
14.3%
G 1
14.3%
Decimal Number
ValueCountFrequency (%)
1 4
44.4%
8 2
22.2%
3 2
22.2%
2 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
＠ 5
83.3%
@ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
104
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 916
85.1%
Common 153
 
14.2%
Latin 7
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 82
 
9.0%
체 81
 
8.8%
육 81
 
8.8%
설 80
 
8.7%
수 33
 
3.6%
터 31
 
3.4%
동 30
 
3.3%
약 26
 
2.8%
학 24
 
2.6%
방 14
 
1.5%
Other values (155) 434
47.4%
Common
ValueCountFrequency (%)
104
68.0%
) 17
 
11.1%
( 17
 
11.1%
＠ 5
 
3.3%
1 4
 
2.6%
8 2
 
1.3%
3 2
 
1.3%
@ 1
 
0.7%
2 1
 
0.7%
Latin
ValueCountFrequency (%)
N 1
14.3%
H 1
14.3%
C 1
14.3%
I 1
14.3%
L 1
14.3%
P 1
14.3%
G 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 916
85.1%
ASCII 155
 
14.4%
None 5
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
104
67.1%
) 17
 
11.0%
( 17
 
11.0%
1 4
 
2.6%
8 2
 
1.3%
3 2
 
1.3%
N 1
 
0.6%
H 1
 
0.6%
C 1
 
0.6%
I 1
 
0.6%
Other values (5) 5
 
3.2%
Hangul
ValueCountFrequency (%)
시 82
 
9.0%
체 81
 
8.8%
육 81
 
8.8%
설 80
 
8.7%
수 33
 
3.6%
터 31
 
3.4%
동 30
 
3.3%
약 26
 
2.8%
학 24
 
2.6%
방 14
 
1.5%
Other values (155) 434
47.4%
None
ValueCountFrequency (%)
＠ 5
100.0%

소재지
Text

Distinct84
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size812.0 B
2024-04-06T17:44:28.713219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27
Mean length18.564706
Min length6

Characters and Unicode

Total characters1578
Distinct characters169
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)97.6%

Sample

1st row삼락동 336-1(금강화학 건너)
2nd row삼락동 303-4(서부주유소 건너편)
3rd row삼락동 529-2(삼락동사입구 건너 낙동제방 위)
4th row삼락동 690(삼락생태공원 출구)
5th row모라1동 산91(덕진정)
ValueCountFrequency (%)
학장동 15
 
5.6%
뒤 11
 
4.1%
주례2동 8
 
3.0%
위 8
 
3.0%
감전동 8
 
3.0%
모라3동 7
 
2.6%
괘법동 6
 
2.2%
주례1동 6
 
2.2%
엄궁동 6
 
2.2%
삼락동 6
 
2.2%
Other values (154) 188
69.9%
2024-04-06T17:44:29.833412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184
 
11.7%
1 102
 
6.5%
동 99
 
6.3%
) 64
 
4.1%
( 64
 
4.1%
- 62
 
3.9%
3 52
 
3.3%
산 47
 
3.0%
2 46
 
2.9%
6 40
 
2.5%
Other values (159) 818
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 795
50.4%
Decimal Number 378
24.0%
Space Separator 184
 
11.7%
Close Punctuation 64
 
4.1%
Open Punctuation 64
 
4.1%
Dash Punctuation 62
 
3.9%
Other Punctuation 27
 
1.7%
Uppercase Letter 3
 
0.2%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 99
 
12.5%
산 47
 
5.9%
주 27
 
3.4%
학 25
 
3.1%
장 24
 
3.0%
례 21
 
2.6%
로 21
 
2.6%
대 20
 
2.5%
라 19
 
2.4%
모 18
 
2.3%
Other values (137) 474
59.6%
Decimal Number
ValueCountFrequency (%)
1 102
27.0%
3 52
13.8%
2 46
12.2%
6 40
 
10.6%
5 30
 
7.9%
9 25
 
6.6%
0 24
 
6.3%
4 24
 
6.3%
8 20
 
5.3%
7 15
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 14
51.9%
＠ 10
37.0%
@ 2
 
7.4%
& 1
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
G 1
33.3%
I 1
33.3%
Space Separator
ValueCountFrequency (%)
184
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 62
100.0%
Lowercase Letter
ValueCountFrequency (%)
y 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 792
50.2%
Common 779
49.4%
Latin 4
 
0.3%
Han 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 99
 
12.5%
산 47
 
5.9%
주 27
 
3.4%
학 25
 
3.2%
장 24
 
3.0%
례 21
 
2.7%
로 21
 
2.7%
대 20
 
2.5%
라 19
 
2.4%
모 18
 
2.3%
Other values (136) 471
59.5%
Common
ValueCountFrequency (%)
184
23.6%
1 102
13.1%
) 64
 
8.2%
( 64
 
8.2%
- 62
 
8.0%
3 52
 
6.7%
2 46
 
5.9%
6 40
 
5.1%
5 30
 
3.9%
9 25
 
3.2%
Other values (8) 110
14.1%
Latin
ValueCountFrequency (%)
S 1
25.0%
G 1
25.0%
y 1
25.0%
I 1
25.0%
Han
ValueCountFrequency (%)
內 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 792
50.2%
ASCII 773
49.0%
None 10
 
0.6%
CJK 3
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
184
23.8%
1 102
13.2%
) 64
 
8.3%
( 64
 
8.3%
- 62
 
8.0%
3 52
 
6.7%
2 46
 
6.0%
6 40
 
5.2%
5 30
 
3.9%
9 25
 
3.2%
Other values (11) 104
13.5%
Hangul
ValueCountFrequency (%)
동 99
 
12.5%
산 47
 
5.9%
주 27
 
3.4%
학 25
 
3.2%
장 24
 
3.0%
례 21
 
2.7%
로 21
 
2.7%
대 20
 
2.5%
라 19
 
2.4%
모 18
 
2.3%
Other values (136) 471
59.5%
None
ValueCountFrequency (%)
＠ 10
100.0%
CJK
ValueCountFrequency (%)
內 3
100.0%

소재지 위도
Real number (ℝ)

Distinct74
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.96128
Minimum128.09225
Maximum129.0156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-04-06T17:44:30.214344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.09225
5-th percentile128.96757
Q1128.98146
median128.99303
Q3129.00348
95-th percentile129.01046
Maximum129.0156
Range0.9233503
Interquartile range (IQR)0.0220253

Descriptive statistics

Standard deviation0.16765381
Coefficient of variation (CV)0.0013000321
Kurtosis24.576399
Mean128.96128
Median Absolute Deviation (MAD)0.0109616
Skewness-5.0828804
Sum10961.709
Variance0.028107799
MonotonicityNot monotonic
2024-04-06T17:44:30.580829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0104585 4
 
4.7%
128.9954145 2
 
2.4%
128.9718335 2
 
2.4%
128.9900082 2
 
2.4%
128.9982374 2
 
2.4%
129.00477 2
 
2.4%
129.0042 2
 
2.4%
128.9973979 2
 
2.4%
128.0923433 2
 
2.4%
129.0021013 1
 
1.2%
Other values (64) 64
75.3%
ValueCountFrequency (%)
128.0922546 1
1.2%
128.0923433 2
2.4%
128.9626756 1
1.2%
128.9670063 1
1.2%
128.9698267 1
1.2%
128.9718335 2
2.4%
128.9737085 1
1.2%
128.9756281 1
1.2%
128.9758526 1
1.2%
128.9762407 1
1.2%
ValueCountFrequency (%)
129.0156049 1
 
1.2%
129.0139009 1
 
1.2%
129.0112435 1
 
1.2%
129.0106729 1
 
1.2%
129.0104585 4
4.7%
129.0100706 1
 
1.2%
129.0094771 1
 
1.2%
129.0089781 1
 
1.2%
129.0087563 1
 
1.2%
129.0087509 1
 
1.2%

소재지 경도
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.19123
Minimum35.120133
Maximum36.145024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-04-06T17:44:30.965178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.120133
5-th percentile35.12874
Q135.141663
median35.153514
Q335.175494
95-th percentile35.191226
Maximum36.145024
Range1.0248913
Interquartile range (IQR)0.0338306

Descriptive statistics

Standard deviation0.18447691
Coefficient of variation (CV)0.0052421273
Kurtosis24.294476
Mean35.19123
Median Absolute Deviation (MAD)0.0147649
Skewness5.0410153
Sum2991.2546
Variance0.03403173
MonotonicityNot monotonic
2024-04-06T17:44:31.343709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1416632 4
 
4.7%
35.1615956 2
 
2.4%
35.1517218 2
 
2.4%
35.1289797 2
 
2.4%
35.191435 2
 
2.4%
35.1707779 2
 
2.4%
35.1414911 2
 
2.4%
35.1754938 2
 
2.4%
36.1450245 2
 
2.4%
35.1822054 1
 
1.2%
Other values (64) 64
75.3%
ValueCountFrequency (%)
35.1201332 1
1.2%
35.1258857 1
1.2%
35.1264107 1
1.2%
35.1284685 1
1.2%
35.12868 1
1.2%
35.1289797 2
2.4%
35.12956 1
1.2%
35.1321478 1
1.2%
35.1341864 1
1.2%
35.1357394 1
1.2%
ValueCountFrequency (%)
36.1450245 2
2.4%
36.1447707 1
1.2%
35.191435 2
2.4%
35.1903876 1
1.2%
35.1901586 1
1.2%
35.1901482 1
1.2%
35.1881422 1
1.2%
35.1861027 1
1.2%
35.1858563 1
1.2%
35.184022 1
1.2%

설치유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size812.0 B
동네
39 
약수터
26 
등산로
15 
학교
5 

Length

Max length3
Median length2
Mean length2.4823529
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동네
2nd row동네
3rd row동네
4th row동네
5th row등산로

Common Values

ValueCountFrequency (%)
동네 39
45.9%
약수터 26
30.6%
등산로 15
 
17.6%
학교 5
 
5.9%

Length

2024-04-06T17:44:31.707086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:44:32.082570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동네 39
45.9%
약수터 26
30.6%
등산로 15
 
17.6%
학교 5
 
5.9%

체력단련시설(운동기구) 시설종류(종)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9294118
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-04-06T17:44:32.441312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q14
median8
Q311
95-th percentile21.8
Maximum26
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.1194231
Coefficient of variation (CV)0.68531089
Kurtosis0.56632079
Mean8.9294118
Median Absolute Deviation (MAD)4
Skewness1.1364084
Sum759
Variance37.447339
MonotonicityNot monotonic
2024-04-06T17:44:32.859704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 11
12.9%
9 10
11.8%
3 9
10.6%
5 7
 
8.2%
8 6
 
7.1%
7 6
 
7.1%
14 5
 
5.9%
2 4
 
4.7%
19 4
 
4.7%
6 4
 
4.7%
Other values (11) 19
22.4%
ValueCountFrequency (%)
1 1
 
1.2%
2 4
 
4.7%
3 9
10.6%
4 11
12.9%
5 7
8.2%
6 4
 
4.7%
7 6
7.1%
8 6
7.1%
9 10
11.8%
10 4
 
4.7%
ValueCountFrequency (%)
26 2
 
2.4%
24 1
 
1.2%
22 2
 
2.4%
21 1
 
1.2%
20 1
 
1.2%
19 4
4.7%
18 1
 
1.2%
16 1
 
1.2%
14 5
5.9%
13 2
 
2.4%

체력단련시설(운동기구) 설치대수(점)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.023529
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-04-06T17:44:33.189835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q14
median8
Q314
95-th percentile25.8
Maximum41
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7949564
Coefficient of variation (CV)0.77766583
Kurtosis2.4246791
Mean10.023529
Median Absolute Deviation (MAD)4
Skewness1.5197757
Sum852
Variance60.761345
MonotonicityNot monotonic
2024-04-06T17:44:33.572975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4 11
12.9%
3 9
10.6%
10 6
 
7.1%
8 6
 
7.1%
11 6
 
7.1%
7 6
 
7.1%
14 5
 
5.9%
6 5
 
5.9%
5 5
 
5.9%
2 4
 
4.7%
Other values (16) 22
25.9%
ValueCountFrequency (%)
1 1
 
1.2%
2 4
 
4.7%
3 9
10.6%
4 11
12.9%
5 5
5.9%
6 5
5.9%
7 6
7.1%
8 6
7.1%
9 3
 
3.5%
10 6
7.1%
ValueCountFrequency (%)
41 1
1.2%
30 1
1.2%
28 1
1.2%
27 1
1.2%
26 1
1.2%
25 2
2.4%
23 2
2.4%
21 1
1.2%
20 1
1.2%
19 1
1.2%

간이운동시설(테니스장 등) 시설종류(종)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
0
75 
<NA>
9 
1
 
1

Length

Max length4
Median length1
Mean length1.3176471
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75
88.2%
<NA> 9
 
10.6%
1 1
 
1.2%

Length

2024-04-06T17:44:34.083529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:44:34.476302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 75
88.2%
na 9
 
10.6%
1 1
 
1.2%

간이운동시설(테니스장 등) 설치대수(점)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
0
75 
<NA>
9 
3
 
1

Length

Max length4
Median length1
Mean length1.3176471
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75
88.2%
<NA> 9
 
10.6%
3 1
 
1.2%

Length

2024-04-06T17:44:34.869441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:44:35.211804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 75
88.2%
na 9
 
10.6%
3 1
 
1.2%

최초설치연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)31.0%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2006.5714
Minimum1992
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-04-06T17:44:35.563602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1995
Q11999
median2005.5
Q32013
95-th percentile2020
Maximum2022
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3222531
Coefficient of variation (CV)0.0041474991
Kurtosis-1.2530938
Mean2006.5714
Median Absolute Deviation (MAD)6.5
Skewness0.13501849
Sum168552
Variance69.259897
MonotonicityNot monotonic
2024-04-06T17:44:35.983487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1997 9
 
10.6%
1999 9
 
10.6%
2012 7
 
8.2%
2013 6
 
7.1%
2001 5
 
5.9%
2005 4
 
4.7%
2017 4
 
4.7%
2016 4
 
4.7%
2002 4
 
4.7%
2000 4
 
4.7%
Other values (16) 28
32.9%
ValueCountFrequency (%)
1992 1
 
1.2%
1993 1
 
1.2%
1994 2
 
2.4%
1995 2
 
2.4%
1997 9
10.6%
1999 9
10.6%
2000 4
4.7%
2001 5
5.9%
2002 4
4.7%
2004 1
 
1.2%
ValueCountFrequency (%)
2022 3
3.5%
2020 3
3.5%
2019 1
 
1.2%
2017 4
4.7%
2016 4
4.7%
2015 2
 
2.4%
2014 2
 
2.4%
2013 6
7.1%
2012 7
8.2%
2011 3
3.5%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean423.30588
Minimum15
Maximum6449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-04-06T17:44:36.379765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile30
Q160
median150
Q3350
95-th percentile980
Maximum6449
Range6434
Interquartile range (IQR)290

Descriptive statistics

Standard deviation959.49032
Coefficient of variation (CV)2.2666595
Kurtosis24.959307
Mean423.30588
Median Absolute Deviation (MAD)100
Skewness4.8335161
Sum35981
Variance920621.67
MonotonicityNot monotonic
2024-04-06T17:44:36.765994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
150 7
 
8.2%
200 7
 
8.2%
100 7
 
8.2%
30 5
 
5.9%
50 5
 
5.9%
900 4
 
4.7%
250 3
 
3.5%
60 3
 
3.5%
40 3
 
3.5%
300 3
 
3.5%
Other values (31) 38
44.7%
ValueCountFrequency (%)
15 1
 
1.2%
20 1
 
1.2%
27 1
 
1.2%
30 5
5.9%
33 1
 
1.2%
40 3
3.5%
44 1
 
1.2%
48 1
 
1.2%
50 5
5.9%
51 1
 
1.2%
ValueCountFrequency (%)
6449 1
 
1.2%
4744 1
 
1.2%
4147 1
 
1.2%
1200 1
 
1.2%
1000 1
 
1.2%
900 4
4.7%
850 1
 
1.2%
750 1
 
1.2%
660 1
 
1.2%
650 1
 
1.2%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
Minimum2024-03-26 00:00:00
Maximum2024-03-26 00:00:00
2024-04-06T17:44:37.076784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:37.322231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-06T17:44:22.251682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:10.602440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:12.388362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:14.349788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:16.331825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:18.483752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:20.544435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:22.468720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:10.835226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:12.651760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:14.588092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:16.619963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:18.730302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:20.765859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:22.753726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:11.067992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:12.901825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:14.811237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:16.896475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:18.975600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:20.999321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:23.001354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:11.345379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:13.202206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:15.034012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:17.172221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:19.280980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:21.256542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:23.253827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:11.591921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:13.533645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:15.313241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:17.442086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:19.567803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:21.471421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:23.534347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:11.868994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:13.826291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:15.666109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:17.767578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:19.912614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:21.748598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:23.831150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:12.084407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:14.073052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:16.007072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:18.201789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:20.243443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:44:21.987343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:44:37.542534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설명소재지소재지 위도소재지 경도설치유형체력단련시설(운동기구) 시설종류(종)체력단련시설(운동기구) 설치대수(점)간이운동시설(테니스장 등) 시설종류(종)간이운동시설(테니스장 등) 설치대수(점)최초설치연도면적(제곱미터)
연번1.0001.0001.0000.0000.3210.7010.6380.4510.0000.0000.8970.345
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.000
소재지 위도0.0001.0001.0001.0000.9180.0000.0000.0000.0000.0000.2140.000
소재지 경도0.3211.0001.0000.9181.0000.1360.0000.0000.0000.0000.4010.000
설치유형0.7011.0001.0000.0000.1361.0000.5230.3170.0000.0000.8030.000
체력단련시설(운동기구) 시설종류(종)0.6381.0000.0000.0000.0000.5231.0000.8930.0000.0000.7770.415
체력단련시설(운동기구) 설치대수(점)0.4511.0000.0000.0000.0000.3170.8931.0000.0000.0000.5710.227
간이운동시설(테니스장 등) 시설종류(종)0.0001.0001.0000.0000.0000.0000.0000.0001.0000.6880.0001.000
간이운동시설(테니스장 등) 설치대수(점)0.0001.0001.0000.0000.0000.0000.0000.0000.6881.0000.0001.000
최초설치연도0.8971.0001.0000.2140.4010.8030.7770.5710.0000.0001.0000.380
면적(제곱미터)0.3451.0001.0000.0000.0000.0000.4150.2271.0001.0000.3801.000
2024-04-06T17:44:37.907886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
간이운동시설(테니스장 등) 시설종류(종)간이운동시설(테니스장 등) 설치대수(점)설치유형
간이운동시설(테니스장 등) 시설종류(종)1.0000.4830.000
간이운동시설(테니스장 등) 설치대수(점)0.4831.0000.000
설치유형0.0000.0001.000
2024-04-06T17:44:38.177231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소재지 위도소재지 경도체력단련시설(운동기구) 시설종류(종)체력단련시설(운동기구) 설치대수(점)최초설치연도면적(제곱미터)설치유형간이운동시설(테니스장 등) 시설종류(종)간이운동시설(테니스장 등) 설치대수(점)
연번1.000-0.238-0.528-0.495-0.4960.592-0.5030.4830.0000.000
소재지 위도-0.2381.0000.1150.1710.189-0.2330.2320.0870.0000.000
소재지 경도-0.5280.1151.000-0.026-0.0350.0120.0670.0870.0000.000
체력단련시설(운동기구) 시설종류(종)-0.4950.171-0.0261.0000.991-0.7470.7110.3230.0000.000
체력단련시설(운동기구) 설치대수(점)-0.4960.189-0.0350.9911.000-0.7440.7270.2400.0000.000
최초설치연도0.592-0.2330.012-0.747-0.7441.000-0.6460.6030.0000.000
면적(제곱미터)-0.5030.2320.0670.7110.727-0.6461.0000.0000.9800.980
설치유형0.4830.0870.0870.3230.2400.6030.0001.0000.0000.000
간이운동시설(테니스장 등) 시설종류(종)0.0000.0000.0000.0000.0000.0000.9800.0001.0000.483
간이운동시설(테니스장 등) 설치대수(점)0.0000.0000.0000.0000.0000.0000.9800.0000.4831.000

Missing values

2024-04-06T17:44:24.455363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:44:25.015051image/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.

Sample

연번시설명소재지소재지 위도소재지 경도설치유형체력단련시설(운동기구) 시설종류(종)체력단련시설(운동기구) 설치대수(점)간이운동시설(테니스장 등) 시설종류(종)간이운동시설(테니스장 등) 설치대수(점)최초설치연도면적(제곱미터)데이터기준일
01낙동제방(금강화학) 체육시설삼락동 336-1(금강화학 건너)128.97975235.190159동네10100020081502024-03-26
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34낙동제방(삼락생태공원) 체육시설삼락동 690(삼락생태공원 출구)128.9763835.167515동네10140020121502024-03-26
45모라벽산＠뒤 체육시설모라1동 산91(덕진정)128.99823735.191435등산로14140020012002024-03-26
56백수약수터밑 체육시설모라1동 산91(솔밭배드민턴장)128.99823735.191435약수터11120019992002024-03-26
67모라중학교 체육시설백양대로 936 (모라동, 모라중학교), 모라1동 1348128.99303535.190148학교13160020056602024-03-26
78모라1동네(청소년수련관) 체육시설모라1동 1365-1(덕상로 129,청소년수련관 뒤)128.99019535.183001동네14160019929002024-03-26
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연번시설명소재지소재지 위도소재지 경도설치유형체력단련시설(운동기구) 시설종류(종)체력단련시설(운동기구) 설치대수(점)간이운동시설(테니스장 등) 시설종류(종)간이운동시설(테니스장 등) 설치대수(점)최초설치연도면적(제곱미터)데이터기준일
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7677새뜰마을 하천데크 체육시설가야대로 331129.00875135.15147동네33<NA><NA>20221222024-03-26
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7980온골마을 행복쉼터 체육시설주례동 919-7129.00045635.152127동네33<NA><NA>2022152024-03-26
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8182학장제방(에스오일LPG충전소 건너편) 체육시설학장동 473-2128.9859535.142476동네44<NA><NA>2012482024-03-26
8283학장제방(현대자동차그린서비스건너편) 체육시설학장동 426-3128.98438835.142043동네44<NA><NA>2012512024-03-26
8384학장제방(엄궁중학교건너편) 체육시설대동로 63128.97562835.136411동네55<NA><NA>2012442024-03-26
8485엄궁동 갈맷길(NH농협은행엄궁시장점 건너편) 체육시설엄궁동 606128.96267635.135739동네33<NA><NA><NA>812024-03-26