Overview

Dataset statistics

Number of variables5
Number of observations30
Missing cells8
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory46.4 B

Variable types

Text3
Numeric2

Dataset

Description전라남도 장성군 체육시설 현황 데이터로 시설명(홍길동체육관, 공설운동장 등), 주소, 부지면적, 건물 총면적, 준공연도 항목을 제공합니다.
URLhttps://www.data.go.kr/data/3033314/fileData.do

Alerts

건물총면적 has 8 (26.7%) missing valuesMissing
시 설 명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:19:50.974893
Analysis finished2023-12-12 17:19:52.129190
Duration1.15 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시 설 명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T02:19:52.328398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length9.2
Min length3

Characters and Unicode

Total characters276
Distinct characters84
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

Unique30 ?
Unique (%)100.0%

Sample

1st row홍길동체육관
2nd row워라벨돔경기장(테니스장)
3rd row군민회관
4th row공설운동장
5th row장성호조정경기장
ValueCountFrequency (%)
게이트볼장 4
 
10.0%
황룡강변 3
 
7.5%
그라운드골프장 3
 
7.5%
홍길동체육관 1
 
2.5%
생활체육공원족구장 1
 
2.5%
생활체육공원배구장 1
 
2.5%
생활체육공원풋살장 1
 
2.5%
삼계풋살장 1
 
2.5%
삼계테니스장 1
 
2.5%
상무평화공원 1
 
2.5%
Other values (23) 23
57.5%
2023-12-13T02:19:52.801071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 30
 
10.9%
이 13
 
4.7%
게 12
 
4.3%
트 12
 
4.3%
전 12
 
4.3%
천 12
 
4.3%
후 12
 
4.3%
볼 12
 
4.3%
10
 
3.6%
삼 7
 
2.5%
Other values (74) 144
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
95.7%
Space Separator 10
 
3.6%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 30
 
11.4%
이 13
 
4.9%
게 12
 
4.5%
트 12
 
4.5%
전 12
 
4.5%
천 12
 
4.5%
후 12
 
4.5%
볼 12
 
4.5%
삼 7
 
2.7%
공 5
 
1.9%
Other values (71) 137
51.9%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
95.7%
Common 12
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 30
 
11.4%
이 13
 
4.9%
게 12
 
4.5%
트 12
 
4.5%
전 12
 
4.5%
천 12
 
4.5%
후 12
 
4.5%
볼 12
 
4.5%
삼 7
 
2.7%
공 5
 
1.9%
Other values (71) 137
51.9%
Common
ValueCountFrequency (%)
10
83.3%
( 1
 
8.3%
) 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 264
95.7%
ASCII 12
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 30
 
11.4%
이 13
 
4.9%
게 12
 
4.5%
트 12
 
4.5%
전 12
 
4.5%
천 12
 
4.5%
후 12
 
4.5%
볼 12
 
4.5%
삼 7
 
2.7%
공 5
 
1.9%
Other values (71) 137
51.9%
ASCII
ValueCountFrequency (%)
10
83.3%
( 1
 
8.3%
) 1
 
8.3%

주소
Text

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T02:19:53.058214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length21.633333
Min length20

Characters and Unicode

Total characters649
Distinct characters55
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 (%)80.0%

Sample

1st row전라남도 장성군 장성읍 기산리 388
2nd row전라남도 장성군 장성읍 기산리 388
3rd row전라남도 장성군 장성읍 영천리 1485-5
4th row전라남도 장성군 장성읍 기산리 461
5th row전라남도 장성군 북이면 수성리 377
ValueCountFrequency (%)
전라남도 30
20.1%
장성군 30
20.1%
장성읍 11
 
7.4%
기산리 10
 
6.7%
삼계면 6
 
4.0%
주산리 5
 
3.4%
황룡면 4
 
2.7%
388 2
 
1.3%
140-7 2
 
1.3%
461 2
 
1.3%
Other values (44) 47
31.5%
2023-12-13T02:19:53.468030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120
18.5%
장 43
 
6.6%
성 42
 
6.5%
남 32
 
4.9%
전 30
 
4.6%
군 30
 
4.6%
라 30
 
4.6%
리 30
 
4.6%
도 30
 
4.6%
1 26
 
4.0%
Other values (45) 236
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 389
59.9%
Space Separator 120
 
18.5%
Decimal Number 118
 
18.2%
Dash Punctuation 22
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 43
11.1%
성 42
10.8%
남 32
 
8.2%
전 30
 
7.7%
군 30
 
7.7%
라 30
 
7.7%
리 30
 
7.7%
도 30
 
7.7%
면 19
 
4.9%
산 17
 
4.4%
Other values (33) 86
22.1%
Decimal Number
ValueCountFrequency (%)
1 26
22.0%
4 15
12.7%
5 13
11.0%
9 12
10.2%
3 10
 
8.5%
7 10
 
8.5%
2 9
 
7.6%
0 9
 
7.6%
6 8
 
6.8%
8 6
 
5.1%
Space Separator
ValueCountFrequency (%)
120
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 389
59.9%
Common 260
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 43
11.1%
성 42
10.8%
남 32
 
8.2%
전 30
 
7.7%
군 30
 
7.7%
라 30
 
7.7%
리 30
 
7.7%
도 30
 
7.7%
면 19
 
4.9%
산 17
 
4.4%
Other values (33) 86
22.1%
Common
ValueCountFrequency (%)
120
46.2%
1 26
 
10.0%
- 22
 
8.5%
4 15
 
5.8%
5 13
 
5.0%
9 12
 
4.6%
3 10
 
3.8%
7 10
 
3.8%
2 9
 
3.5%
0 9
 
3.5%
Other values (2) 14
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 389
59.9%
ASCII 260
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
46.2%
1 26
 
10.0%
- 22
 
8.5%
4 15
 
5.8%
5 13
 
5.0%
9 12
 
4.6%
3 10
 
3.8%
7 10
 
3.8%
2 9
 
3.5%
0 9
 
3.5%
Other values (2) 14
 
5.4%
Hangul
ValueCountFrequency (%)
장 43
11.1%
성 42
10.8%
남 32
 
8.2%
전 30
 
7.7%
군 30
 
7.7%
라 30
 
7.7%
리 30
 
7.7%
도 30
 
7.7%
면 19
 
4.9%
산 17
 
4.4%
Other values (33) 86
22.1%

부지면적
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8383.3
Minimum892
Maximum74911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T02:19:53.616017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum892
5-th percentile994.05
Q11324.5
median2063
Q36685.5
95-th percentile40764.9
Maximum74911
Range74019
Interquartile range (IQR)5361

Descriptive statistics

Standard deviation16621.127
Coefficient of variation (CV)1.9826473
Kurtosis10.931566
Mean8383.3
Median Absolute Deviation (MAD)1068.5
Skewness3.3131369
Sum251499
Variance2.7626185 × 108
MonotonicityNot monotonic
2023-12-13T02:19:53.757484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3500 2
 
6.7%
57918 1
 
3.3%
13506 1
 
3.3%
1050 1
 
3.3%
2390 1
 
3.3%
1536 1
 
3.3%
9829 1
 
3.3%
74911 1
 
3.3%
7140 1
 
3.3%
3584 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
892 1
3.3%
990 1
3.3%
999 1
3.3%
1039 1
3.3%
1050 1
3.3%
1064 1
3.3%
1125 1
3.3%
1290 1
3.3%
1428 1
3.3%
1531 1
3.3%
ValueCountFrequency (%)
74911 1
3.3%
57918 1
3.3%
19800 1
3.3%
15571 1
3.3%
13506 1
3.3%
9829 1
3.3%
7994 1
3.3%
7140 1
3.3%
5322 1
3.3%
4719 1
3.3%

건물총면적
Text

MISSING 

Distinct20
Distinct (%)90.9%
Missing8
Missing (%)26.7%
Memory size372.0 B
2023-12-13T02:19:53.918102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2272727
Min length1

Characters and Unicode

Total characters71
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)86.4%

Sample

1st row10964
2nd row2812
3rd row1248
4th row10200
5th row474
ValueCountFrequency (%)
498 3
 
14.3%
648 1
 
4.8%
10964 1
 
4.8%
466 1
 
4.8%
2450 1
 
4.8%
65 1
 
4.8%
2625 1
 
4.8%
49 1
 
4.8%
499 1
 
4.8%
455 1
 
4.8%
Other values (9) 9
42.9%
2023-12-13T02:19:54.226217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 16
22.5%
2 10
14.1%
9 8
11.3%
8 8
11.3%
1 7
9.9%
0 7
9.9%
6 7
9.9%
5 6
 
8.5%
7 1
 
1.4%
1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
98.6%
Space Separator 1
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 16
22.9%
2 10
14.3%
9 8
11.4%
8 8
11.4%
1 7
10.0%
0 7
10.0%
6 7
10.0%
5 6
 
8.6%
7 1
 
1.4%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 16
22.5%
2 10
14.1%
9 8
11.3%
8 8
11.3%
1 7
9.9%
0 7
9.9%
6 7
9.9%
5 6
 
8.5%
7 1
 
1.4%
1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 16
22.5%
2 10
14.1%
9 8
11.3%
8 8
11.3%
1 7
9.9%
0 7
9.9%
6 7
9.9%
5 6
 
8.5%
7 1
 
1.4%
1
 
1.4%

준공연도
Real number (ℝ)

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.0667
Minimum1988
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T02:19:54.338655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1988
5-th percentile2001
Q12006.75
median2013
Q32015
95-th percentile2020.1
Maximum2021
Range33
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation7.0658642
Coefficient of variation (CV)0.0035134908
Kurtosis2.5571225
Mean2011.0667
Median Absolute Deviation (MAD)4
Skewness-1.2567112
Sum60332
Variance49.926437
MonotonicityNot monotonic
2023-12-13T02:19:54.462589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2013 7
23.3%
2015 3
10.0%
2001 2
 
6.7%
2009 2
 
6.7%
2019 2
 
6.7%
2006 2
 
6.7%
2014 2
 
6.7%
2005 2
 
6.7%
2021 2
 
6.7%
1988 1
 
3.3%
Other values (5) 5
16.7%
ValueCountFrequency (%)
1988 1
 
3.3%
2001 2
 
6.7%
2002 1
 
3.3%
2005 2
 
6.7%
2006 2
 
6.7%
2009 2
 
6.7%
2010 1
 
3.3%
2011 1
 
3.3%
2013 7
23.3%
2014 2
 
6.7%
ValueCountFrequency (%)
2021 2
 
6.7%
2019 2
 
6.7%
2018 1
 
3.3%
2017 1
 
3.3%
2015 3
10.0%
2014 2
 
6.7%
2013 7
23.3%
2011 1
 
3.3%
2010 1
 
3.3%
2009 2
 
6.7%

Interactions

2023-12-13T02:19:51.631319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:19:51.319049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:19:51.752095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:19:51.480700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:19:54.601943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시 설 명주소부지면적건물총면적준공연도
시 설 명1.0001.0001.0001.0001.000
주소1.0001.0000.0000.9490.850
부지면적1.0000.0001.0001.0000.704
건물총면적1.0000.9491.0001.0000.849
준공연도1.0000.8500.7040.8491.000
2023-12-13T02:19:54.720947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부지면적준공연도
부지면적1.000-0.072
준공연도-0.0721.000

Missing values

2023-12-13T02:19:51.899269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:19:52.071468image/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

시 설 명주소부지면적건물총면적준공연도
0홍길동체육관전라남도 장성군 장성읍 기산리 38857918109642001
1워라벨돔경기장(테니스장)전라남도 장성군 장성읍 기산리 388799428122001
2군민회관전라남도 장성군 장성읍 영천리 1485-5471912481988
3공설운동장전라남도 장성군 장성읍 기산리 46119800102002002
4장성호조정경기장전라남도 장성군 북이면 수성리 3779994742009
5그라운드골프장전라남도 장성군 장성읍 기산리 4613500<NA>2013
6삼계면 그라운드골프장전라남도 장성군 삼계면 주산리 481-13500<NA>2019
7삼서면 그라운드골프장전라남도 장성군 삼계면 대곡리 929-121428<NA>2019
8북일전천후 게이트볼장전라남도 장성군 북일면 신흥리 193-415318642006
9서삼전천후 게이트볼장전라남도 장성군 서삼면 장산리 19920468022006
시 설 명주소부지면적건물총면적준공연도
20황룡강변 생활체육공원배구장전라남도 장성군 장성읍 기산리 140-71064<NA>2013
21황룡강변 생활체육공원풋살장전라남도 장성군 장성읍 기산리 140-61125<NA>2013
22삼계풋살장전라남도 장성군 삼계면 주산리 753-11560<NA>2013
23삼계테니스장전라남도 장성군 삼계면 주산리 744-235842005
24상무평화공원 축구장전라남도 장성군 삼계면 주산리 690-67140<NA>2017
25옐로우시티스타디움전라남도 장성군 장성읍 기산리 447-17491126252021
26파크골프장전라남도 장성군 황룡면 월평리 590-19829652021
27실내수영장전라남도 장성군 장성읍 기산리 135-1153624502014
28북하전천후게이트볼장전라남도 장성군 북하면 중평리526-523904982005
29국궁장전라남도 장성군 황룡면 아곡리 37310504202015