Overview

Dataset statistics

Number of variables12
Number of observations95
Missing cells34
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory101.4 B

Variable types

Numeric4
Categorical3
Text4
DateTime1

Dataset

Description포천시에서 제공하는 공공체육시설 위치, 조성년도, 조성규모 등 안내
Author경기도 포천시
URLhttps://www.data.go.kr/data/15064770/fileData.do

Alerts

데이터기준일 has constant value ""Constant
위도 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
읍면동 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
관리기관 is highly overall correlated with 관리기관 전화번호High correlation
관리기관 전화번호 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
소재지도로명주소 has 33 (34.7%) missing valuesMissing
조성면적(제곱미터) has 1 (1.1%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:07:52.824571
Analysis finished2023-12-12 18:07:56.135146
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T03:07:56.221474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.7
Q124.5
median48
Q371.5
95-th percentile90.3
Maximum95
Range94
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.568098
Coefficient of variation (CV)0.57433536
Kurtosis-1.2
Mean48
Median Absolute Deviation (MAD)24
Skewness0
Sum4560
Variance760
MonotonicityStrictly increasing
2023-12-13T03:07:56.353321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
2 1
 
1.1%
71 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
Other values (85) 85
89.5%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
95 1
1.1%
94 1
1.1%
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%

읍면동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size892.0 B
군내면
13 
소흘읍
10 
신북면
10 
선단동
8 
일동면
7 
Other values (9)
47 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row군내면
2nd row소흘읍
3rd row신북면
4th row일동면
5th row관인면

Common Values

ValueCountFrequency (%)
군내면 13
13.7%
소흘읍 10
10.5%
신북면 10
10.5%
선단동 8
8.4%
일동면 7
7.4%
가산면 7
7.4%
관인면 6
 
6.3%
영북면 6
 
6.3%
창수면 6
 
6.3%
내촌면 6
 
6.3%
Other values (4) 16
16.8%

Length

2023-12-13T03:07:56.508838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
군내면 13
13.7%
소흘읍 10
10.5%
신북면 10
10.5%
선단동 8
8.4%
일동면 7
7.4%
가산면 7
7.4%
관인면 6
 
6.3%
영북면 6
 
6.3%
창수면 6
 
6.3%
내촌면 6
 
6.3%
Other values (4) 16
16.8%

시설명
Text

Distinct94
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size892.0 B
2023-12-13T03:07:56.817833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length9.8947368
Min length5

Characters and Unicode

Total characters940
Distinct characters133
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)97.9%

Sample

1st row포천종합운동장
2nd row소흘읍 생활체육공원축구장
3rd row포천시 자원회수시설 내 축구장
4th row일동 하수처리시설 내 축구장
5th row관인 하수처리시설 내 축구장
ValueCountFrequency (%)
게이트볼장 39
 
21.3%
내 7
 
3.8%
축구장 6
 
3.3%
족구장 4
 
2.2%
하수처리시설 4
 
2.2%
풋살구장 4
 
2.2%
국궁장 4
 
2.2%
영북 3
 
1.6%
포천종합운동장 3
 
1.6%
포천 3
 
1.6%
Other values (96) 106
57.9%
2023-12-13T03:07:57.282095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
 
9.5%
장 85
 
9.0%
이 47
 
5.0%
트 44
 
4.7%
게 43
 
4.6%
볼 43
 
4.6%
리 38
 
4.0%
육 25
 
2.7%
구 24
 
2.6%
동 23
 
2.4%
Other values (123) 479
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 818
87.0%
Space Separator 89
 
9.5%
Decimal Number 20
 
2.1%
Close Punctuation 6
 
0.6%
Open Punctuation 6
 
0.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 85
 
10.4%
이 47
 
5.7%
트 44
 
5.4%
게 43
 
5.3%
볼 43
 
5.3%
리 38
 
4.6%
육 25
 
3.1%
구 24
 
2.9%
동 23
 
2.8%
체 21
 
2.6%
Other values (113) 425
52.0%
Decimal Number
ValueCountFrequency (%)
1 8
40.0%
2 7
35.0%
6 2
 
10.0%
5 1
 
5.0%
7 1
 
5.0%
0 1
 
5.0%
Space Separator
ValueCountFrequency (%)
89
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 818
87.0%
Common 122
 
13.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 85
 
10.4%
이 47
 
5.7%
트 44
 
5.4%
게 43
 
5.3%
볼 43
 
5.3%
리 38
 
4.6%
육 25
 
3.1%
구 24
 
2.9%
동 23
 
2.8%
체 21
 
2.6%
Other values (113) 425
52.0%
Common
ValueCountFrequency (%)
89
73.0%
1 8
 
6.6%
2 7
 
5.7%
) 6
 
4.9%
( 6
 
4.9%
6 2
 
1.6%
5 1
 
0.8%
7 1
 
0.8%
0 1
 
0.8%
- 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 818
87.0%
ASCII 122
 
13.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
73.0%
1 8
 
6.6%
2 7
 
5.7%
) 6
 
4.9%
( 6
 
4.9%
6 2
 
1.6%
5 1
 
0.8%
7 1
 
0.8%
0 1
 
0.8%
- 1
 
0.8%
Hangul
ValueCountFrequency (%)
장 85
 
10.4%
이 47
 
5.7%
트 44
 
5.4%
게 43
 
5.3%
볼 43
 
5.3%
리 38
 
4.6%
육 25
 
3.1%
구 24
 
2.9%
동 23
 
2.8%
체 21
 
2.6%
Other values (113) 425
52.0%

소재지지번주소
Text

Distinct86
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size892.0 B
2023-12-13T03:07:57.740177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length20.642105
Min length15

Characters and Unicode

Total characters1961
Distinct characters108
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)82.1%

Sample

1st row경기도 포천시 군내면 구읍리 691
2nd row경기도 포천시 소흘읍 이동교리 산1
3rd row경기도 포천시 신북면 만세교리 101
4th row 경기도 포천시 일동면 사직리 1390-1
5th row 경기도 포천시 관인면 탄동리 920
ValueCountFrequency (%)
경기도 95
20.4%
포천시 95
20.4%
군내면 13
 
2.8%
소흘읍 10
 
2.2%
신북면 10
 
2.2%
가산면 7
 
1.5%
일동면 7
 
1.5%
내촌면 6
 
1.3%
영중면 6
 
1.3%
관인면 6
 
1.3%
Other values (148) 210
45.2%
2023-12-13T03:07:58.323697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
372
19.0%
천 99
 
5.0%
기 99
 
5.0%
경 95
 
4.8%
도 95
 
4.8%
포 95
 
4.8%
시 95
 
4.8%
1 84
 
4.3%
면 74
 
3.8%
리 72
 
3.7%
Other values (98) 781
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1146
58.4%
Space Separator 372
 
19.0%
Decimal Number 369
 
18.8%
Dash Punctuation 63
 
3.2%
Control 5
 
0.3%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 99
 
8.6%
기 99
 
8.6%
경 95
 
8.3%
도 95
 
8.3%
포 95
 
8.3%
시 95
 
8.3%
면 74
 
6.5%
리 72
 
6.3%
동 33
 
2.9%
내 22
 
1.9%
Other values (83) 367
32.0%
Decimal Number
ValueCountFrequency (%)
1 84
22.8%
2 48
13.0%
5 43
11.7%
3 37
10.0%
6 31
 
8.4%
7 28
 
7.6%
0 27
 
7.3%
9 25
 
6.8%
8 25
 
6.8%
4 21
 
5.7%
Space Separator
ValueCountFrequency (%)
372
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Control
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1146
58.4%
Common 815
41.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 99
 
8.6%
기 99
 
8.6%
경 95
 
8.3%
도 95
 
8.3%
포 95
 
8.3%
시 95
 
8.3%
면 74
 
6.5%
리 72
 
6.3%
동 33
 
2.9%
내 22
 
1.9%
Other values (83) 367
32.0%
Common
ValueCountFrequency (%)
372
45.6%
1 84
 
10.3%
- 63
 
7.7%
2 48
 
5.9%
5 43
 
5.3%
3 37
 
4.5%
6 31
 
3.8%
7 28
 
3.4%
0 27
 
3.3%
9 25
 
3.1%
Other values (5) 57
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1146
58.4%
ASCII 815
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
372
45.6%
1 84
 
10.3%
- 63
 
7.7%
2 48
 
5.9%
5 43
 
5.3%
3 37
 
4.5%
6 31
 
3.8%
7 28
 
3.4%
0 27
 
3.3%
9 25
 
3.1%
Other values (5) 57
 
7.0%
Hangul
ValueCountFrequency (%)
천 99
 
8.6%
기 99
 
8.6%
경 95
 
8.3%
도 95
 
8.3%
포 95
 
8.3%
시 95
 
8.3%
면 74
 
6.5%
리 72
 
6.3%
동 33
 
2.9%
내 22
 
1.9%
Other values (83) 367
32.0%

소재지도로명주소
Text

MISSING 

Distinct56
Distinct (%)90.3%
Missing33
Missing (%)34.7%
Memory size892.0 B
2023-12-13T03:07:58.670719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length20.258065
Min length14

Characters and Unicode

Total characters1256
Distinct characters85
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)80.6%

Sample

1st row경기도 포천시 군내면 호국로 1518
2nd row경기도 포천시 소흘읍 태봉로63번길 91
3rd row경기도 포천시 신북면 신평로16번길 207
4th row경기도 포천시 일동면 수입로 396
5th row경기도 포천시 관인면 창동로 1931
ValueCountFrequency (%)
경기도 62
20.5%
포천시 62
20.5%
소흘읍 8
 
2.6%
영중면 6
 
2.0%
군내면 6
 
2.0%
신북면 6
 
2.0%
영북면 5
 
1.7%
가산면 5
 
1.7%
창수면 5
 
1.7%
이동면 4
 
1.3%
Other values (97) 133
44.0%
2023-12-13T03:07:59.231792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
19.1%
천 64
 
5.1%
기 64
 
5.1%
포 63
 
5.0%
경 62
 
4.9%
도 62
 
4.9%
시 62
 
4.9%
1 49
 
3.9%
면 49
 
3.9%
로 44
 
3.5%
Other values (75) 497
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 770
61.3%
Space Separator 240
 
19.1%
Decimal Number 230
 
18.3%
Dash Punctuation 15
 
1.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 64
 
8.3%
기 64
 
8.3%
포 63
 
8.2%
경 62
 
8.1%
도 62
 
8.1%
시 62
 
8.1%
면 49
 
6.4%
로 44
 
5.7%
길 22
 
2.9%
동 17
 
2.2%
Other values (62) 261
33.9%
Decimal Number
ValueCountFrequency (%)
1 49
21.3%
3 28
12.2%
9 26
11.3%
2 25
10.9%
5 22
9.6%
6 22
9.6%
7 21
9.1%
4 14
 
6.1%
8 12
 
5.2%
0 11
 
4.8%
Space Separator
ValueCountFrequency (%)
240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 770
61.3%
Common 486
38.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 64
 
8.3%
기 64
 
8.3%
포 63
 
8.2%
경 62
 
8.1%
도 62
 
8.1%
시 62
 
8.1%
면 49
 
6.4%
로 44
 
5.7%
길 22
 
2.9%
동 17
 
2.2%
Other values (62) 261
33.9%
Common
ValueCountFrequency (%)
240
49.4%
1 49
 
10.1%
3 28
 
5.8%
9 26
 
5.3%
2 25
 
5.1%
5 22
 
4.5%
6 22
 
4.5%
7 21
 
4.3%
- 15
 
3.1%
4 14
 
2.9%
Other values (3) 24
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 770
61.3%
ASCII 486
38.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
49.4%
1 49
 
10.1%
3 28
 
5.8%
9 26
 
5.3%
2 25
 
5.1%
5 22
 
4.5%
6 22
 
4.5%
7 21
 
4.3%
- 15
 
3.1%
4 14
 
2.9%
Other values (3) 24
 
4.9%
Hangul
ValueCountFrequency (%)
천 64
 
8.3%
기 64
 
8.3%
포 63
 
8.2%
경 62
 
8.1%
도 62
 
8.1%
시 62
 
8.1%
면 49
 
6.4%
로 44
 
5.7%
길 22
 
2.9%
동 17
 
2.2%
Other values (62) 261
33.9%

조성면적(제곱미터)
Text

MISSING 

Distinct58
Distinct (%)61.7%
Missing1
Missing (%)1.1%
Memory size892.0 B
2023-12-13T03:07:59.505656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length4.106383
Min length3

Characters and Unicode

Total characters386
Distinct characters11
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

Unique47 ?
Unique (%)50.0%

Sample

1st row53,401
2nd row54,000
3rd row49,532
4th row41,062
5th row13,123
ValueCountFrequency (%)
300 23
24.5%
500 4
 
4.3%
1,230 3
 
3.2%
550 3
 
3.2%
480 2
 
2.1%
600 2
 
2.1%
54,000 2
 
2.1%
49,532 2
 
2.1%
460 2
 
2.1%
9,673 2
 
2.1%
Other values (48) 49
52.1%
2023-12-13T03:07:59.919346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
27.5%
3 46
11.9%
, 45
11.7%
5 33
 
8.5%
1 29
 
7.5%
4 28
 
7.3%
9 24
 
6.2%
2 21
 
5.4%
7 21
 
5.4%
6 20
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 341
88.3%
Other Punctuation 45
 
11.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
31.1%
3 46
13.5%
5 33
 
9.7%
1 29
 
8.5%
4 28
 
8.2%
9 24
 
7.0%
2 21
 
6.2%
7 21
 
6.2%
6 20
 
5.9%
8 13
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 386
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
27.5%
3 46
11.9%
, 45
11.7%
5 33
 
8.5%
1 29
 
7.5%
4 28
 
7.3%
9 24
 
6.2%
2 21
 
5.4%
7 21
 
5.4%
6 20
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
27.5%
3 46
11.9%
, 45
11.7%
5 33
 
8.5%
1 29
 
7.5%
4 28
 
7.3%
9 24
 
6.2%
2 21
 
5.4%
7 21
 
5.4%
6 20
 
5.2%

조성연도
Real number (ℝ)

Distinct20
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.1474
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T03:08:00.104439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile2003
Q12009
median2010
Q32013
95-th percentile2016
Maximum2019
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7016711
Coefficient of variation (CV)0.0023389684
Kurtosis4.5707085
Mean2010.1474
Median Absolute Deviation (MAD)3
Skewness-1.4691374
Sum190964
Variance22.105711
MonotonicityNot monotonic
2023-12-13T03:08:00.235544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2009 19
20.0%
2010 12
12.6%
2013 10
10.5%
2011 9
9.5%
2014 8
8.4%
2016 5
 
5.3%
2004 5
 
5.3%
2015 5
 
5.3%
2012 5
 
5.3%
2005 3
 
3.2%
Other values (10) 14
14.7%
ValueCountFrequency (%)
1990 1
 
1.1%
1992 1
 
1.1%
2000 1
 
1.1%
2002 1
 
1.1%
2003 2
 
2.1%
2004 5
5.3%
2005 3
3.2%
2006 1
 
1.1%
2007 2
 
2.1%
2008 2
 
2.1%
ValueCountFrequency (%)
2019 2
 
2.1%
2018 1
 
1.1%
2016 5
 
5.3%
2015 5
 
5.3%
2014 8
8.4%
2013 10
10.5%
2012 5
 
5.3%
2011 9
9.5%
2010 12
12.6%
2009 19
20.0%

관리기관
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
포천시
79 
포천도시공사
16 

Length

Max length6
Median length3
Mean length3.5052632
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포천도시공사
2nd row포천도시공사
3rd row포천도시공사
4th row포천시
5th row포천시

Common Values

ValueCountFrequency (%)
포천시 79
83.2%
포천도시공사 16
 
16.8%

Length

2023-12-13T03:08:00.413079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:00.554180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포천시 79
83.2%
포천도시공사 16
 
16.8%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.944425
Minimum37.767492
Maximum38.886781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T03:08:00.710742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.767492
5-th percentile37.798709
Q137.85425
median37.934759
Q338.010102
95-th percentile38.149844
Maximum38.886781
Range1.1192882
Interquartile range (IQR)0.1558518

Descriptive statistics

Standard deviation0.14105496
Coefficient of variation (CV)0.0037174095
Kurtosis20.145549
Mean37.944425
Median Absolute Deviation (MAD)0.0793994
Skewness3.3201484
Sum3604.7203
Variance0.019896503
MonotonicityNot monotonic
2023-12-13T03:08:00.883769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.8885384 3
 
3.2%
37.8584981 3
 
3.2%
37.824242 2
 
2.1%
37.9364141 2
 
2.1%
38.0849554 2
 
2.1%
37.83857 2
 
2.1%
38.0410403 2
 
2.1%
37.9749161 1
 
1.1%
37.9545188 1
 
1.1%
37.904824 1
 
1.1%
Other values (76) 76
80.0%
ValueCountFrequency (%)
37.7674924 1
1.1%
37.7679593 1
1.1%
37.7866476 1
1.1%
37.7868463 1
1.1%
37.7871155 1
1.1%
37.8036777 1
1.1%
37.8162739 1
1.1%
37.8195729 1
1.1%
37.8211318 1
1.1%
37.8212582 1
1.1%
ValueCountFrequency (%)
38.8867806 1
1.1%
38.1655194 1
1.1%
38.1622918 1
1.1%
38.1529468 1
1.1%
38.1528929 1
1.1%
38.1485372 1
1.1%
38.1481745 1
1.1%
38.090867 1
1.1%
38.0869023 1
1.1%
38.0857493 1
1.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.22971
Minimum127.12465
Maximum128.20117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T03:08:01.093633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.12465
5-th percentile127.12697
Q1127.18524
median127.21289
Q3127.25244
95-th percentile127.34362
Maximum128.20117
Range1.076518
Interquartile range (IQR)0.067198

Descriptive statistics

Standard deviation0.11812954
Coefficient of variation (CV)0.00092847452
Kurtosis48.979923
Mean127.22971
Median Absolute Deviation (MAD)0.032149
Skewness6.0517214
Sum12086.822
Variance0.013954588
MonotonicityNot monotonic
2023-12-13T03:08:01.266382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.200585 3
 
3.2%
127.139789 3
 
3.2%
127.125494 2
 
2.1%
127.225649 2
 
2.1%
127.273759 2
 
2.1%
127.185239 2
 
2.1%
127.368248 2
 
2.1%
127.274296 1
 
1.1%
127.320951 1
 
1.1%
127.212886 1
 
1.1%
Other values (76) 76
80.0%
ValueCountFrequency (%)
127.124654 1
 
1.1%
127.125494 2
2.1%
127.125655 1
 
1.1%
127.126728 1
 
1.1%
127.127079 1
 
1.1%
127.127306 1
 
1.1%
127.13723 1
 
1.1%
127.138406 1
 
1.1%
127.139789 3
3.2%
127.14334 1
 
1.1%
ValueCountFrequency (%)
128.201172 1
1.1%
127.370517 1
1.1%
127.368735 1
1.1%
127.368248 2
2.1%
127.333059 1
1.1%
127.33223 1
1.1%
127.329473 1
1.1%
127.326623 1
1.1%
127.320951 1
1.1%
127.313639 1
1.1%

관리기관 전화번호
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size892.0 B
031-538-4303
7 
031-538-4204
 
6
031-538-4504
 
6
031-538-4453
 
6
031-538-4355
 
6
Other values (21)
64 

Length

Max length13
Median length12
Mean length12.010526
Min length12

Unique

Unique6 ?
Unique (%)6.3%

Sample

1st row031-540-6332
2nd row031-540-6381
3rd row031-540-6381
4th row031-538-3533
5th row031-538-3533

Common Values

ValueCountFrequency (%)
031-538-4303 7
 
7.4%
031-538-4204 6
 
6.3%
031-538-4504 6
 
6.3%
031-538-4453 6
 
6.3%
031-538-4355 6
 
6.3%
031-538-4803 5
 
5.3%
031-538-4253 5
 
5.3%
031-538-4654 5
 
5.3%
031-538-4403 5
 
5.3%
031-540-6381 5
 
5.3%
Other values (16) 39
41.1%

Length

2023-12-13T03:08:01.419733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
031-538-4303 7
 
7.4%
031-538-4504 6
 
6.3%
031-538-4453 6
 
6.3%
031-538-4355 6
 
6.3%
031-538-4204 6
 
6.3%
031-538-4803 5
 
5.3%
031-538-4253 5
 
5.3%
031-538-4654 5
 
5.3%
031-538-4403 5
 
5.3%
031-540-6381 5
 
5.3%
Other values (16) 39
41.1%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
Minimum2020-09-15 00:00:00
Maximum2020-09-15 00:00:00
2023-12-13T03:08:01.547919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:01.662566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T03:07:55.407968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.918291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.265661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.688302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.477455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.008603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.368135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.775426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.561434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.095003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.485166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.892040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.650939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.173760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.597558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.006011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:08:01.762178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동시설명소재지지번주소소재지도로명주소조성면적(제곱미터)조성연도관리기관위도경도관리기관 전화번호
연번1.0000.4921.0000.8530.9110.7830.3250.6040.5080.1800.795
읍면동0.4921.0001.0001.0001.0000.7900.0000.5290.9170.8480.993
시설명1.0001.0001.0000.9961.0001.0000.8441.0001.0001.0001.000
소재지지번주소0.8531.0000.9961.0001.0000.9660.0001.0000.0000.0000.976
소재지도로명주소0.9111.0001.0001.0001.0000.9800.3131.0001.0001.0000.644
조성면적(제곱미터)0.7830.7901.0000.9660.9801.0000.7770.9950.2660.4430.917
조성연도0.3250.0000.8440.0000.3130.7771.0000.2340.0000.0000.051
관리기관0.6040.5291.0001.0001.0000.9950.2341.0000.1860.3280.998
위도0.5080.9171.0000.0001.0000.2660.0000.1861.0000.7430.927
경도0.1800.8481.0000.0001.0000.4430.0000.3280.7431.0000.886
관리기관 전화번호0.7950.9931.0000.9760.6440.9170.0510.9980.9270.8861.000
2023-12-13T03:08:01.900234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관읍면동관리기관 전화번호
관리기관1.0000.3870.828
읍면동0.3871.0000.867
관리기관 전화번호0.8280.8671.000
2023-12-13T03:08:02.011522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번조성연도위도경도읍면동관리기관관리기관 전화번호
연번1.0000.292-0.084-0.0990.2140.4460.391
조성연도0.2921.0000.0260.0300.0850.2770.000
위도-0.0840.0261.0000.6310.7390.2230.661
경도-0.0990.0300.6311.0000.6300.2160.601
읍면동0.2140.0850.7390.6301.0000.3870.867
관리기관0.4460.2770.2230.2160.3871.0000.828
관리기관 전화번호0.3910.0000.6610.6010.8670.8281.000

Missing values

2023-12-13T03:07:55.779594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:07:55.969150image/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.
2023-12-13T03:07:56.083453image/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

연번읍면동시설명소재지지번주소소재지도로명주소조성면적(제곱미터)조성연도관리기관위도경도관리기관 전화번호데이터기준일
01군내면포천종합운동장경기도 포천시 군내면 구읍리 691경기도 포천시 군내면 호국로 151853,4011990포천도시공사37.888538127.200585031-540-63322020-09-15
12소흘읍소흘읍 생활체육공원축구장경기도 포천시 소흘읍 이동교리 산1경기도 포천시 소흘읍 태봉로63번길 9154,0002009포천도시공사37.824242127.125494031-540-63812020-09-15
23신북면포천시 자원회수시설 내 축구장경기도 포천시 신북면 만세교리 101경기도 포천시 신북면 신평로16번길 20749,5322009포천도시공사37.967614127.2436031-540-63812020-09-15
34일동면일동 하수처리시설 내 축구장경기도 포천시 일동면 사직리 1390-1경기도 포천시 일동면 수입로 39641,0622010포천시38.0127.313327031-538-35332020-09-15
45관인면관인 하수처리시설 내 축구장경기도 포천시 관인면 탄동리 920경기도 포천시 관인면 창동로 193113,1232011포천시38.162292127.256406031-538-35332020-09-15
56영북면영북 하수처리시설 내 축구장경기도 포천시 영북면 문암리 276경기도 포천시 영북면 호국로 39619,8302004포천시38.086902127.265468031-538-35332020-09-15
67선단동설운체육공원 내 축구장경기도 포천시 설운동 559-28경기도 포천시 해룡로 79?15,8622004포천시37.858498127.139789031-538-48032020-09-15
78신북면포천축구공원 내 축구장경기도 포천시 신북면 신평로16번길 207-1 (우)11138경기도 포천시 신북면 만세교리 산691,4402015포천도시공사37.967845127.244875031-540-63812020-09-15
89군내면포천종합운동장 보조구장경기도 포천시 군내면 호국로 1518경기도 포천시 군내면 구읍리18,0552015포천도시공사37.888538127.200585031-540-63322020-09-15
910창수면포천 야구장경기도 포천시 창수면 전영로 1184경기도 포천시 창수면 오가리 94229,3992013포천도시공사38.01763127.191493031-538-63602020-09-15
연번읍면동시설명소재지지번주소소재지도로명주소조성면적(제곱미터)조성연도관리기관위도경도관리기관 전화번호데이터기준일
8586영북면영북면 족구장경기도 포천시 영북면 운천리 585-4<NA>6402012포천시38.090867127.265117031-358-46032020-09-15
8687선단동풋살구장(동교동)경기도 포천시 동교동 154-10<NA>1,2302014포천시37.844563127.13723031-538-48032020-09-15
8788가산면풋살구장(가산면)경기도 포천시 가산면 마산리 286-1경기도 포천시 가산면 마정로69번길 13-441,1152012포천시37.83857127.185239031-538-43032020-09-15
8889소흘읍아리솔청소년체육광장경기도 포천시 소흘읍 송우리 726-2경기도 포천시 소흘읍 태봉로 2442,9362011포천시37.832333127.148567031-540-64302020-09-15
8990소흘읍소흘읍 족구장경기도 포천시 소흘읍 이동교리 781-22<NA>1,9422016포천시37.819573127.14334031-540-64302020-09-15
9091선단동선단동 족구장경기도 포천시 설운동 173-5경기도 포천시 호국로883번길 911052016포천시37.84634127.150742031-538-48032020-09-15
9192군내면군내 풋살구장경기도 포천시 군내면 하성북리 645경기도 포천시 군내면 포천로 14941,2302009포천시37.891874127.210019031-538-42042020-09-15
9293군내면포천탁구장경기도 포천시 군내면 용정리 321경기도 포천시 군내면 호국로 1518-13,5062018포천도시공사37.886854127.200637031-540-63322020-09-15
9394내촌면내촌체력단련장경기도 포천시 내촌면 내리 521-35경기도 포천시 내촌면 내촌로 73-259922019포천시37.787115127.227343031-538-42532020-09-15
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