Overview

Dataset statistics

Number of variables7
Number of observations76
Missing cells91
Missing cells (%)17.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory60.7 B

Variable types

Categorical2
Text2
Numeric2
Unsupported1

Dataset

Description창녕군시설관리공단 체육시설물(군립수영장, 창녕스포츠파크, 국민체육센터) 내 헬스장에 보유중인 체력단련 장비 현황(품명, 규격, 수량 등) 데이터를 제공합니다.
Author창녕군시설관리공단
URLhttps://www.data.go.kr/data/15026718/fileData.do

Alerts

수량 is highly overall correlated with 단위High correlation
구입년도 is highly overall correlated with 구 분High correlation
구 분 is highly overall correlated with 구입년도High correlation
단위 is highly overall correlated with 수량High correlation
단위 is highly imbalanced (77.9%)Imbalance
규 격 has 15 (19.7%) missing valuesMissing
비고 has 76 (100.0%) missing valuesMissing
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 20:53:10.771217
Analysis finished2023-12-12 20:53:11.689028
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size740.0 B
군민체육관
27 
국민체육센터
25 
스포츠파크
24 

Length

Max length6
Median length5
Mean length5.3289474
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군민체육관 27
35.5%
국민체육센터 25
32.9%
스포츠파크 24
31.6%

Length

2023-12-13T05:53:11.764715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:53:11.882236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군민체육관 27
35.5%
국민체육센터 25
32.9%
스포츠파크 24
31.6%

품명
Text

Distinct69
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-13T05:53:12.126501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length11.618421
Min length2

Characters and Unicode

Total characters883
Distinct characters111
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

Unique62 ?
Unique (%)81.6%

Sample

1st row런닝머신
2nd row더블트위스트(허리돌리기)
3rd row상체근력강화기(체스트프레스머신)
4th row상체근력강화기(버터플라이머신)
5th row하체근력강화기(레그익스텐션머신)
ValueCountFrequency (%)
런닝머신 2
 
2.5%
아령 2
 
2.5%
러닝머신 2
 
2.5%
하체근력강화기(레그프레스머신 2
 
2.5%
체중계 2
 
2.5%
상체체근력강화기(스미스머신 2
 
2.5%
중량원판세트 2
 
2.5%
하체근력강화기(레그익스텐션 2
 
2.5%
정리대 2
 
2.5%
상체근력강화기(체스트프레스 1
 
1.2%
Other values (61) 61
76.2%
2023-12-13T05:53:12.584646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
체 68
 
7.7%
기 55
 
6.2%
근 52
 
5.9%
강 52
 
5.9%
화 52
 
5.9%
( 52
 
5.9%
) 52
 
5.9%
력 50
 
5.7%
스 34
 
3.9%
상 32
 
3.6%
Other values (101) 384
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 775
87.8%
Open Punctuation 52
 
5.9%
Close Punctuation 52
 
5.9%
Space Separator 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
체 68
 
8.8%
기 55
 
7.1%
근 52
 
6.7%
강 52
 
6.7%
화 52
 
6.7%
력 50
 
6.5%
스 34
 
4.4%
상 32
 
4.1%
레 26
 
3.4%
머 24
 
3.1%
Other values (98) 330
42.6%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 775
87.8%
Common 108
 
12.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
체 68
 
8.8%
기 55
 
7.1%
근 52
 
6.7%
강 52
 
6.7%
화 52
 
6.7%
력 50
 
6.5%
스 34
 
4.4%
상 32
 
4.1%
레 26
 
3.4%
머 24
 
3.1%
Other values (98) 330
42.6%
Common
ValueCountFrequency (%)
( 52
48.1%
) 52
48.1%
4
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 775
87.8%
ASCII 108
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
체 68
 
8.8%
기 55
 
7.1%
근 52
 
6.7%
강 52
 
6.7%
화 52
 
6.7%
력 50
 
6.5%
스 34
 
4.4%
상 32
 
4.1%
레 26
 
3.4%
머 24
 
3.1%
Other values (98) 330
42.6%
ASCII
ValueCountFrequency (%)
( 52
48.1%
) 52
48.1%
4
 
3.7%

규 격
Text

MISSING 

Distinct48
Distinct (%)78.7%
Missing15
Missing (%)19.7%
Memory size740.0 B
2023-12-13T05:53:12.824825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length8
Mean length7.3278689
Min length3

Characters and Unicode

Total characters447
Distinct characters46
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)73.8%

Sample

1st rowSTA-4000T
2nd rowSTAF-021
3rd rowSTA-7001
4th rowSTA-7004
5th rowSTA-7014
ValueCountFrequency (%)
sta 12
 
16.0%
7000 11
 
14.7%
set 3
 
4.0%
헬스라인 2
 
2.7%
pr501 1
 
1.3%
s25tl 1
 
1.3%
sta-4000t 1
 
1.3%
ds-312 1
 
1.3%
ds-314 1
 
1.3%
ds-302 1
 
1.3%
Other values (41) 41
54.7%
2023-12-13T05:53:13.219315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 89
19.9%
S 49
11.0%
T 33
 
7.4%
1 32
 
7.2%
A 30
 
6.7%
- 29
 
6.5%
7 25
 
5.6%
14
 
3.1%
3 13
 
2.9%
D 12
 
2.7%
Other values (36) 121
27.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 199
44.5%
Uppercase Letter 161
36.0%
Dash Punctuation 29
 
6.5%
Lowercase Letter 22
 
4.9%
Other Letter 16
 
3.6%
Space Separator 14
 
3.1%
Other Punctuation 6
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 49
30.4%
T 33
20.5%
A 30
18.6%
D 12
 
7.5%
F 11
 
6.8%
L 5
 
3.1%
W 4
 
2.5%
M 3
 
1.9%
P 2
 
1.2%
K 2
 
1.2%
Other values (8) 10
 
6.2%
Decimal Number
ValueCountFrequency (%)
0 89
44.7%
1 32
 
16.1%
7 25
 
12.6%
3 13
 
6.5%
2 12
 
6.0%
5 11
 
5.5%
8 5
 
2.5%
4 4
 
2.0%
9 4
 
2.0%
6 4
 
2.0%
Other Letter
ValueCountFrequency (%)
스 3
18.8%
헬 3
18.8%
인 2
12.5%
라 2
12.5%
모 1
 
6.2%
닝 1
 
6.2%
반 1
 
6.2%
토 1
 
6.2%
우 1
 
6.2%
아 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
g 7
31.8%
k 6
27.3%
m 3
13.6%
t 3
13.6%
e 3
13.6%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 248
55.5%
Latin 183
40.9%
Hangul 16
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 49
26.8%
T 33
18.0%
A 30
16.4%
D 12
 
6.6%
F 11
 
6.0%
g 7
 
3.8%
k 6
 
3.3%
L 5
 
2.7%
W 4
 
2.2%
m 3
 
1.6%
Other values (13) 23
12.6%
Common
ValueCountFrequency (%)
0 89
35.9%
1 32
 
12.9%
- 29
 
11.7%
7 25
 
10.1%
14
 
5.6%
3 13
 
5.2%
2 12
 
4.8%
5 11
 
4.4%
, 6
 
2.4%
8 5
 
2.0%
Other values (3) 12
 
4.8%
Hangul
ValueCountFrequency (%)
스 3
18.8%
헬 3
18.8%
인 2
12.5%
라 2
12.5%
모 1
 
6.2%
닝 1
 
6.2%
반 1
 
6.2%
토 1
 
6.2%
우 1
 
6.2%
아 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431
96.4%
Hangul 16
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89
20.6%
S 49
11.4%
T 33
 
7.7%
1 32
 
7.4%
A 30
 
7.0%
- 29
 
6.7%
7 25
 
5.8%
14
 
3.2%
3 13
 
3.0%
D 12
 
2.8%
Other values (26) 105
24.4%
Hangul
ValueCountFrequency (%)
스 3
18.8%
헬 3
18.8%
인 2
12.5%
라 2
12.5%
모 1
 
6.2%
닝 1
 
6.2%
반 1
 
6.2%
토 1
 
6.2%
우 1
 
6.2%
아 1
 
6.2%

수량
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0789474
Minimum1
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T05:53:13.353217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5.75
Maximum238
Range237
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.472592
Coefficient of variation (CV)4.8483052
Kurtosis53.75826
Mean6.0789474
Median Absolute Deviation (MAD)0
Skewness7.1203417
Sum462
Variance868.63368
MonotonicityNot monotonic
2023-12-13T05:53:13.455668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 67
88.2%
2 4
 
5.3%
3 1
 
1.3%
32 1
 
1.3%
14 1
 
1.3%
100 1
 
1.3%
238 1
 
1.3%
ValueCountFrequency (%)
1 67
88.2%
2 4
 
5.3%
3 1
 
1.3%
14 1
 
1.3%
32 1
 
1.3%
100 1
 
1.3%
238 1
 
1.3%
ValueCountFrequency (%)
238 1
 
1.3%
100 1
 
1.3%
32 1
 
1.3%
14 1
 
1.3%
3 1
 
1.3%
2 4
 
5.3%
1 67
88.2%

단위
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size740.0 B
EA
72 
KG
 
2
SET
 
2

Length

Max length3
Median length2
Mean length2.0263158
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
EA 72
94.7%
KG 2
 
2.6%
SET 2
 
2.6%

Length

2023-12-13T05:53:13.590104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:53:13.715873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ea 72
94.7%
kg 2
 
2.6%
set 2
 
2.6%

구입년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.4211
Minimum2005
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T05:53:13.820723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12005
median2012.5
Q32015
95-th percentile2015
Maximum2017
Range12
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.2528834
Coefficient of variation (CV)0.0021154193
Kurtosis-1.5978277
Mean2010.4211
Median Absolute Deviation (MAD)3.5
Skewness-0.18072292
Sum152792
Variance18.087018
MonotonicityNot monotonic
2023-12-13T05:53:13.962012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2005 24
31.6%
2015 19
25.0%
2013 16
21.1%
2009 13
17.1%
2017 2
 
2.6%
2016 1
 
1.3%
2012 1
 
1.3%
ValueCountFrequency (%)
2005 24
31.6%
2009 13
17.1%
2012 1
 
1.3%
2013 16
21.1%
2015 19
25.0%
2016 1
 
1.3%
2017 2
 
2.6%
ValueCountFrequency (%)
2017 2
 
2.6%
2016 1
 
1.3%
2015 19
25.0%
2013 16
21.1%
2012 1
 
1.3%
2009 13
17.1%
2005 24
31.6%

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing76
Missing (%)100.0%
Memory size816.0 B

Interactions

2023-12-13T05:53:11.276445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:11.124897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:11.364895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:11.197082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:53:14.074529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분품명규 격수량단위구입년도
구 분1.0000.7611.0000.0000.4200.715
품명0.7611.0000.8360.0001.0000.000
규 격1.0000.8361.0001.000NaN0.981
수량0.0000.0001.0001.0000.6650.000
단위0.4201.000NaN0.6651.000NaN
구입년도0.7150.0000.9810.000NaN1.000
2023-12-13T05:53:14.209737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분단위
구 분1.0000.156
단위0.1561.000
2023-12-13T05:53:14.332442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수량구입년도구 분단위
수량1.0000.0950.0000.688
구입년도0.0951.0000.8510.000
구 분0.0000.8511.0000.156
단위0.6880.0000.1561.000

Missing values

2023-12-13T05:53:11.500454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:53:11.637450image/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

구 분품명규 격수량단위구입년도비고
0국민체육센터런닝머신STA-4000T2EA2013<NA>
1국민체육센터더블트위스트(허리돌리기)STAF-0211EA2013<NA>
2국민체육센터상체근력강화기(체스트프레스머신)STA-70013EA2013<NA>
3국민체육센터상체근력강화기(버터플라이머신)STA-70041EA2013<NA>
4국민체육센터하체근력강화기(레그익스텐션머신)STA-70141EA2013<NA>
5국민체육센터하체근력강화기(래그컬머신)STA-70151EA2013<NA>
6국민체육센터상체근력강화기(스미스머신)STA-70191EA2013<NA>
7국민체육센터각도조절벤치STAF-0081EA2013<NA>
8국민체육센터평벤치STAF-0071EA2013<NA>
9국민체육센터경량벤치STAF-0161EA2013<NA>
구 분품명규 격수량단위구입년도비고
66군민체육관바벨거치대<NA>1EA2005<NA>
67군민체육관경량컬바<NA>100KG2005<NA>
68군민체육관아령거치대<NA>1EA2005<NA>
69군민체육관아령<NA>238KG2005<NA>
70군민체육관중량컬러고무판<NA>1SET2005<NA>
71군민체육관경량원판<NA>1SET2005<NA>
72군민체육관벨트마사지헬스모닝9121EA2015<NA>
73군민체육관휴먼바이브레이션MM1001EA2005<NA>
74군민체육관런닝머신S25TL2EA2012<NA>
75군민체육관체중계CAS150KG1EA2005<NA>