Overview

Dataset statistics

Number of variables13
Number of observations86
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory113.5 B

Variable types

Numeric6
Text2
Categorical4
DateTime1

Dataset

Description부산광역시사상구_동네체육시설현황_20230329
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3078733

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 소재지 경도 and 3 other fieldsHigh correlation
소재지 경도 is highly overall correlated with 연번High correlation
체력단련시설(운동기구) 시설종류(종) is highly overall correlated with 연번 and 2 other fieldsHigh correlation
체력단련시설(운동기구) 설치대수(점) is highly overall correlated with 연번 and 2 other fieldsHigh correlation
최초설치연도 is highly overall correlated with 연번 and 3 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
면적(제곱미터) is highly overall correlated with 간이운동시설(테니스장 등) 시설종류(종) and 1 other fieldsHigh correlation
간이운동시설(테니스장 등) 시설종류(종) is highly imbalanced (63.8%)Imbalance
간이운동시설(테니스장 등) 설치대수(점) is highly imbalanced (63.8%)Imbalance
최초설치연도 has 1 (1.2%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:34:49.447655
Analysis finished2023-12-10 16:34:54.110996
Duration4.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.5
Minimum1
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T01:34:54.180633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.25
Q122.25
median43.5
Q364.75
95-th percentile81.75
Maximum86
Range85
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation24.969982
Coefficient of variation (CV)0.57402257
Kurtosis-1.2
Mean43.5
Median Absolute Deviation (MAD)21.5
Skewness0
Sum3741
Variance623.5
MonotonicityStrictly increasing
2023-12-11T01:34:54.317090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
56 1
 
1.2%
64 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%
Other values (76) 76
88.4%
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 (%)
86 1
1.2%
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%

시설명
Text

UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-11T01:34:54.635524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length12.674419
Min length7

Characters and Unicode

Total characters1090
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

Unique86 ?
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 76
39.6%
동네체육시설 4
 
2.1%
옆 3
 
1.6%
건너편 3
 
1.6%
모라3동 2
 
1.0%
주례2동 2
 
1.0%
밑 2
 
1.0%
새뜰마을 2
 
1.0%
온골마을 2
 
1.0%
승학약수터 2
 
1.0%
Other values (94) 94
49.0%
2023-12-11T01:34:55.079350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
106
 
9.7%
시 83
 
7.6%
육 82
 
7.5%
체 82
 
7.5%
설 81
 
7.4%
수 33
 
3.0%
터 31
 
2.8%
동 31
 
2.8%
약 26
 
2.4%
학 24
 
2.2%
Other values (171) 511
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 927
85.0%
Space Separator 106
 
9.7%
Close Punctuation 17
 
1.6%
Open Punctuation 17
 
1.6%
Decimal Number 10
 
0.9%
Uppercase Letter 7
 
0.6%
Other Punctuation 6
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 83
 
9.0%
육 82
 
8.8%
체 82
 
8.8%
설 81
 
8.7%
수 33
 
3.6%
터 31
 
3.3%
동 31
 
3.3%
약 26
 
2.8%
학 24
 
2.6%
방 14
 
1.5%
Other values (155) 440
47.5%
Uppercase Letter
ValueCountFrequency (%)
H 1
14.3%
N 1
14.3%
G 1
14.3%
P 1
14.3%
L 1
14.3%
I 1
14.3%
C 1
14.3%
Decimal Number
ValueCountFrequency (%)
1 4
40.0%
8 2
20.0%
2 2
20.0%
3 2
20.0%
Other Punctuation
ValueCountFrequency (%)
＠ 5
83.3%
@ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
106
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 927
85.0%
Common 156
 
14.3%
Latin 7
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 83
 
9.0%
육 82
 
8.8%
체 82
 
8.8%
설 81
 
8.7%
수 33
 
3.6%
터 31
 
3.3%
동 31
 
3.3%
약 26
 
2.8%
학 24
 
2.6%
방 14
 
1.5%
Other values (155) 440
47.5%
Common
ValueCountFrequency (%)
106
67.9%
) 17
 
10.9%
( 17
 
10.9%
＠ 5
 
3.2%
1 4
 
2.6%
8 2
 
1.3%
2 2
 
1.3%
3 2
 
1.3%
@ 1
 
0.6%
Latin
ValueCountFrequency (%)
H 1
14.3%
N 1
14.3%
G 1
14.3%
P 1
14.3%
L 1
14.3%
I 1
14.3%
C 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 927
85.0%
ASCII 158
 
14.5%
None 5
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
106
67.1%
) 17
 
10.8%
( 17
 
10.8%
1 4
 
2.5%
8 2
 
1.3%
2 2
 
1.3%
3 2
 
1.3%
@ 1
 
0.6%
H 1
 
0.6%
N 1
 
0.6%
Other values (5) 5
 
3.2%
Hangul
ValueCountFrequency (%)
시 83
 
9.0%
육 82
 
8.8%
체 82
 
8.8%
설 81
 
8.7%
수 33
 
3.6%
터 31
 
3.3%
동 31
 
3.3%
약 26
 
2.8%
학 24
 
2.6%
방 14
 
1.5%
Other values (155) 440
47.5%
None
ValueCountFrequency (%)
＠ 5
100.0%

소재지
Text

Distinct85
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-11T01:34:55.350914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27
Mean length18.604651
Min length6

Characters and Unicode

Total characters1600
Distinct characters174
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

Unique84 ?
Unique (%)97.7%

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
 
5.5%
뒤 12
 
4.4%
주례2동 9
 
3.3%
위 8
 
2.9%
감전동 8
 
2.9%
모라3동 7
 
2.6%
엄궁동 6
 
2.2%
삼락동 6
 
2.2%
주례1동 6
 
2.2%
괘법동 6
 
2.2%
Other values (155) 189
69.5%
2023-12-11T01:34:55.780809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
186
 
11.6%
1 104
 
6.5%
동 100
 
6.2%
) 65
 
4.1%
( 65
 
4.1%
- 63
 
3.9%
3 54
 
3.4%
2 48
 
3.0%
산 47
 
2.9%
6 40
 
2.5%
Other values (164) 828
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 806
50.4%
Decimal Number 384
24.0%
Space Separator 186
 
11.6%
Close Punctuation 65
 
4.1%
Open Punctuation 65
 
4.1%
Dash Punctuation 63
 
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 100
 
12.4%
산 47
 
5.8%
주 28
 
3.5%
학 25
 
3.1%
장 24
 
3.0%
례 22
 
2.7%
로 21
 
2.6%
대 20
 
2.5%
라 19
 
2.4%
모 18
 
2.2%
Other values (142) 482
59.8%
Decimal Number
ValueCountFrequency (%)
1 104
27.1%
3 54
14.1%
2 48
12.5%
6 40
 
10.4%
5 30
 
7.8%
9 25
 
6.5%
0 24
 
6.2%
4 24
 
6.2%
8 20
 
5.2%
7 15
 
3.9%
Other Punctuation
ValueCountFrequency (%)
, 14
51.9%
＠ 10
37.0%
@ 2
 
7.4%
& 1
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
G 1
33.3%
S 1
33.3%
I 1
33.3%
Space Separator
ValueCountFrequency (%)
186
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 65
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Lowercase Letter
ValueCountFrequency (%)
y 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 803
50.2%
Common 790
49.4%
Latin 4
 
0.2%
Han 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 100
 
12.5%
산 47
 
5.9%
주 28
 
3.5%
학 25
 
3.1%
장 24
 
3.0%
례 22
 
2.7%
로 21
 
2.6%
대 20
 
2.5%
라 19
 
2.4%
모 18
 
2.2%
Other values (141) 479
59.7%
Common
ValueCountFrequency (%)
186
23.5%
1 104
13.2%
) 65
 
8.2%
( 65
 
8.2%
- 63
 
8.0%
3 54
 
6.8%
2 48
 
6.1%
6 40
 
5.1%
5 30
 
3.8%
9 25
 
3.2%
Other values (8) 110
13.9%
Latin
ValueCountFrequency (%)
G 1
25.0%
S 1
25.0%
y 1
25.0%
I 1
25.0%
Han
ValueCountFrequency (%)
內 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 803
50.2%
ASCII 784
49.0%
None 10
 
0.6%
CJK 3
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
186
23.7%
1 104
13.3%
) 65
 
8.3%
( 65
 
8.3%
- 63
 
8.0%
3 54
 
6.9%
2 48
 
6.1%
6 40
 
5.1%
5 30
 
3.8%
9 25
 
3.2%
Other values (11) 104
13.3%
Hangul
ValueCountFrequency (%)
동 100
 
12.5%
산 47
 
5.9%
주 28
 
3.5%
학 25
 
3.1%
장 24
 
3.0%
례 22
 
2.7%
로 21
 
2.6%
대 20
 
2.5%
라 19
 
2.4%
모 18
 
2.2%
Other values (141) 479
59.7%
None
ValueCountFrequency (%)
＠ 10
100.0%
CJK
ValueCountFrequency (%)
內 3
100.0%

소재지 위도
Real number (ℝ)

Distinct75
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.96187
Minimum128.09225
Maximum129.0156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T01:34:55.921726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.09225
5-th percentile128.96771
Q1128.9816
median128.9936
Q3129.00375
95-th percentile129.01062
Maximum129.0156
Range0.9233503
Interquartile range (IQR)0.02215135

Descriptive statistics

Standard deviation0.1667554
Coefficient of variation (CV)0.0012930597
Kurtosis24.899212
Mean128.96187
Median Absolute Deviation (MAD)0.0105592
Skewness-5.1138551
Sum11090.721
Variance0.027807363
MonotonicityNot monotonic
2023-12-11T01:34:56.085934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0104585 4
 
4.7%
128.9954145 2
 
2.3%
128.9718335 2
 
2.3%
128.9900082 2
 
2.3%
128.9982374 2
 
2.3%
129.0042 2
 
2.3%
129.00477 2
 
2.3%
128.9973979 2
 
2.3%
128.0923433 2
 
2.3%
129.0021013 1
 
1.2%
Other values (65) 65
75.6%
ValueCountFrequency (%)
128.0922546 1
1.2%
128.0923433 2
2.3%
128.9626756 1
1.2%
128.9670063 1
1.2%
128.9698267 1
1.2%
128.9718335 2
2.3%
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%
129.0122813 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%

소재지 경도
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.190771
Minimum35.120133
Maximum36.145024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T01:34:56.266573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.120133
5-th percentile35.128755
Q135.141663
median35.153481
Q335.174315
95-th percentile35.191173
Maximum36.145024
Range1.0248913
Interquartile range (IQR)0.032651625

Descriptive statistics

Standard deviation0.18343791
Coefficient of variation (CV)0.005212671
Kurtosis24.622131
Mean35.190771
Median Absolute Deviation (MAD)0.0146646
Skewness5.0729837
Sum3026.4063
Variance0.033649468
MonotonicityNot monotonic
2023-12-11T01:34:56.447170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1416632 4
 
4.7%
35.1615956 2
 
2.3%
35.1517218 2
 
2.3%
35.1289797 2
 
2.3%
35.191435 2
 
2.3%
35.1414911 2
 
2.3%
35.1707779 2
 
2.3%
35.1754938 2
 
2.3%
36.1450245 2
 
2.3%
35.1822054 1
 
1.2%
Other values (65) 65
75.6%
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
2.3%
35.12956 1
1.2%
35.1321478 1
1.2%
35.1341864 1
1.2%
35.1357394 1
1.2%
ValueCountFrequency (%)
36.1450245 2
2.3%
36.1447707 1
1.2%
35.191435 2
2.3%
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 size820.0 B
동네
40 
약수터
26 
등산로
15 
학교
5 

Length

Max length3
Median length2
Mean length2.4767442
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동네 40
46.5%
약수터 26
30.2%
등산로 15
 
17.4%
학교 5
 
5.8%

Length

2023-12-11T01:34:56.584430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:34:56.706622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동네 40
46.5%
약수터 26
30.2%
등산로 15
 
17.4%
학교 5
 
5.8%

체력단련시설(운동기구) 시설종류(종)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2209302
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T01:34:56.833289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median8
Q311
95-th percentile21.5
Maximum27
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.1728079
Coefficient of variation (CV)0.66943439
Kurtosis0.44539115
Mean9.2209302
Median Absolute Deviation (MAD)4
Skewness1.0696381
Sum793
Variance38.103557
MonotonicityNot monotonic
2023-12-11T01:34:56.995281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 11
12.8%
8 8
9.3%
6 8
9.3%
4 8
9.3%
9 7
 
8.1%
11 6
 
7.0%
5 5
 
5.8%
15 5
 
5.8%
20 5
 
5.8%
10 4
 
4.7%
Other values (12) 19
22.1%
ValueCountFrequency (%)
1 1
 
1.2%
2 3
 
3.5%
3 11
12.8%
4 8
9.3%
5 5
5.8%
6 8
9.3%
7 4
 
4.7%
8 8
9.3%
9 7
8.1%
10 4
 
4.7%
ValueCountFrequency (%)
27 1
 
1.2%
26 1
 
1.2%
24 1
 
1.2%
22 2
 
2.3%
20 5
5.8%
19 1
 
1.2%
18 1
 
1.2%
16 1
 
1.2%
15 5
5.8%
13 2
 
2.3%

체력단련시설(운동기구) 설치대수(점)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.348837
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T01:34:57.153300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median8
Q313.75
95-th percentile25.75
Maximum42
Range41
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation7.9124138
Coefficient of variation (CV)0.76457033
Kurtosis2.5309473
Mean10.348837
Median Absolute Deviation (MAD)4
Skewness1.5120321
Sum890
Variance62.606293
MonotonicityNot monotonic
2023-12-11T01:34:57.309019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3 11
12.8%
8 8
 
9.3%
4 8
 
9.3%
11 7
 
8.1%
6 7
 
8.1%
9 6
 
7.0%
7 4
 
4.7%
15 4
 
4.7%
5 4
 
4.7%
2 3
 
3.5%
Other values (18) 24
27.9%
ValueCountFrequency (%)
1 1
 
1.2%
2 3
 
3.5%
3 11
12.8%
4 8
9.3%
5 4
 
4.7%
6 7
8.1%
7 4
 
4.7%
8 8
9.3%
9 6
7.0%
10 1
 
1.2%
ValueCountFrequency (%)
42 1
 
1.2%
32 1
 
1.2%
29 1
 
1.2%
28 1
 
1.2%
26 1
 
1.2%
25 1
 
1.2%
24 1
 
1.2%
23 2
2.3%
20 3
3.5%
19 1
 
1.2%

간이운동시설(테니스장 등) 시설종류(종)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size820.0 B
0
76 
<NA>
9 
1
 
1

Length

Max length4
Median length1
Mean length1.3139535
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 76
88.4%
<NA> 9
 
10.5%
1 1
 
1.2%

Length

2023-12-11T01:34:57.469712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:34:57.836557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 76
88.4%
na 9
 
10.5%
1 1
 
1.2%

간이운동시설(테니스장 등) 설치대수(점)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size820.0 B
0
76 
<NA>
9 
3
 
1

Length

Max length4
Median length1
Mean length1.3139535
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 76
88.4%
<NA> 9
 
10.5%
3 1
 
1.2%

Length

2023-12-11T01:34:57.967545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:34:58.080332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 76
88.4%
na 9
 
10.5%
3 1
 
1.2%

최초설치연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)30.6%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2006.6235
Minimum1992
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T01:34:58.196973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1995
Q11999
median2006
Q32013
95-th percentile2020
Maximum2022
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.2865015
Coefficient of variation (CV)0.0041295746
Kurtosis-1.2458783
Mean2006.6235
Median Absolute Deviation (MAD)7
Skewness0.1176505
Sum170563
Variance68.666106
MonotonicityNot monotonic
2023-12-11T01:34:58.313718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1997 9
 
10.5%
1999 9
 
10.5%
2012 7
 
8.1%
2013 6
 
7.0%
2001 5
 
5.8%
2005 4
 
4.7%
2017 4
 
4.7%
2016 4
 
4.7%
2011 4
 
4.7%
2002 4
 
4.7%
Other values (16) 29
33.7%
ValueCountFrequency (%)
1992 1
 
1.2%
1993 1
 
1.2%
1994 2
 
2.3%
1995 2
 
2.3%
1997 9
10.5%
1999 9
10.5%
2000 4
4.7%
2001 5
5.8%
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
 
2.3%
2014 2
 
2.3%
2013 6
7.0%
2012 7
8.1%
2011 4
4.7%

면적(제곱미터)
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Memory size820.0 B
100
8 
200
7 
150
7 
50
 
5
30
 
5
Other values (36)
54 

Length

Max length6
Median length4
Mean length3.7906977
Min length3

Unique

Unique26 ?
Unique (%)30.2%

Sample

1st row150
2nd row50
3rd row150
4th row150
5th row200

Common Values

ValueCountFrequency (%)
100 8
 
9.3%
200 7
 
8.1%
150 7
 
8.1%
50 5
 
5.8%
30 5
 
5.8%
900 4
 
4.7%
250 3
 
3.5%
40 3
 
3.5%
500 3
 
3.5%
300 3
 
3.5%
Other values (31) 38
44.2%

Length

2023-12-11T01:34:58.437056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100 8
 
9.3%
150 7
 
8.1%
200 7
 
8.1%
50 5
 
5.8%
30 5
 
5.8%
900 4
 
4.7%
250 3
 
3.5%
40 3
 
3.5%
500 3
 
3.5%
300 3
 
3.5%
Other values (31) 38
44.2%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
Minimum2023-03-29 00:00:00
Maximum2023-03-29 00:00:00
2023-12-11T01:34:58.584732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:58.724796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T01:34:53.291611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:50.489703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.130488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.900690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.356550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.804112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:53.369105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:50.587971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.464970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.975854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.430306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.871774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:53.439540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:50.696283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.543241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.051193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.503698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.953478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:53.517272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:50.797454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.625859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.130564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.575482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:53.029484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:53.598705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:50.894361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.719800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.207360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.650766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:53.120486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:53.678578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.022757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:51.819739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.280683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:52.722297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:34:53.209735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:34:58.833221image/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)
연번1.0001.0001.0000.0000.3260.6470.5420.4840.0000.0000.8920.824
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0000.997
소재지 위도0.0001.0001.0001.0000.9180.0000.0000.0000.0000.0000.2200.563
소재지 경도0.3261.0001.0000.9181.0000.1260.0000.0000.0000.0000.4050.275
설치유형0.6471.0001.0000.0000.1261.0000.5140.3400.0000.0000.8040.344
체력단련시설(운동기구) 시설종류(종)0.5421.0000.0000.0000.0000.5141.0000.8670.0000.0000.7310.760
체력단련시설(운동기구) 설치대수(점)0.4841.0001.0000.0000.0000.3400.8671.0000.0000.0000.5810.863
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
최초설치연도0.8921.0001.0000.2200.4050.8040.7310.5810.0000.0001.0000.858
면적(제곱미터)0.8241.0000.9970.5630.2750.3440.7600.8631.0001.0000.8581.000
2023-12-11T01:34:59.077321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)간이운동시설(테니스장 등) 시설종류(종)간이운동시설(테니스장 등) 설치대수(점)설치유형
면적(제곱미터)1.0000.7570.7570.112
간이운동시설(테니스장 등) 시설종류(종)0.7571.0000.4830.000
간이운동시설(테니스장 등) 설치대수(점)0.7570.4831.0000.000
설치유형0.1120.0000.0001.000
2023-12-11T01:34:59.236358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소재지 위도소재지 경도체력단련시설(운동기구) 시설종류(종)체력단련시설(운동기구) 설치대수(점)최초설치연도설치유형간이운동시설(테니스장 등) 시설종류(종)간이운동시설(테니스장 등) 설치대수(점)면적(제곱미터)
연번1.000-0.238-0.530-0.514-0.5100.5900.4300.0000.0000.344
소재지 위도-0.2381.0000.1090.1500.171-0.2220.0800.0000.0000.152
소재지 경도-0.5300.1091.000-0.031-0.0390.0110.0800.0000.0000.152
체력단련시설(운동기구) 시설종류(종)-0.5140.150-0.0311.0000.991-0.7700.3190.0000.0000.283
체력단련시설(운동기구) 설치대수(점)-0.5100.171-0.0390.9911.000-0.7620.2140.0000.0000.404
최초설치연도0.590-0.2220.011-0.770-0.7621.0000.6050.0000.0000.364
설치유형0.4300.0800.0800.3190.2140.6051.0000.0000.0000.112
간이운동시설(테니스장 등) 시설종류(종)0.0000.0000.0000.0000.0000.0000.0001.0000.4830.757
간이운동시설(테니스장 등) 설치대수(점)0.0000.0000.0000.0000.0000.0000.0000.4831.0000.757
면적(제곱미터)0.3440.1520.1520.2830.4040.3640.1120.7570.7571.000

Missing values

2023-12-11T01:34:53.813895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:34:54.037298image/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)128.97975235.190159동네11110020081502023-03-29
12낙동제방(서부주유소) 체육시설삼락동 303-4(서부주유소 건너편)128.97624135.184022동네44002013502023-03-29
23낙동제방(삼락동사) 체육시설삼락동 529-2(삼락동사입구 건너 낙동제방 위)128.97585335.176906동네20200019941502023-03-29
34낙동제방(삼락생태공원) 체육시설삼락동 690(삼락생태공원 출구)128.9763835.167515동네11150020121502023-03-29
45모라벽산＠뒤 체육시설모라1동 산91(덕진정)128.99823735.191435등산로15150020012002023-03-29
56백수약수터밑 체육시설모라1동 산91(솔밭배드민턴장)128.99823735.191435약수터12130019992002023-03-29
67모라중학교 체육시설백양대로 936 (모라동, 모라중학교), 모라1동 1348128.99303535.190148학교13160020056602023-03-29
78모라1동네(청소년수련관) 체육시설모라1동 1365-1(덕상로 129,청소년수련관 뒤)128.99019535.183001동네15170019929002023-03-29
89모덕초등학교 체육시설모라1동 1364-1(덕상로 120, 모덕초등 內)128.99133435.182178학교440020079002023-03-29
910모라3동 돌산 체육시설모라3동 산93(모라1동 우성2차＠ 뒤편)129.00348435.188142등산로440020131002023-03-29
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