Overview

Dataset statistics

Number of variables5
Number of observations276
Missing cells2
Missing cells (%)0.1%
Duplicate rows4
Duplicate rows (%)1.4%
Total size in memory11.4 KiB
Average record size in memory42.5 B

Variable types

Text3
Numeric2

Dataset

Description영천시 금호읍, 청통면, 신녕면, 화산면, 화북면, 화남면, 자양면, 임고면, 고경면, 북안면, 대창면, 동부동, 중앙동, 서부동, 완산동, 남부동의 동네체육시설(야외운동기구) 설치 현황입니다.
Author경상북도 영천시
URLhttps://www.data.go.kr/data/15084331/fileData.do

Alerts

Dataset has 4 (1.4%) duplicate rowsDuplicates

Reproduction

Analysis started2024-03-14 08:44:46.279541
Analysis finished2024-03-14 08:44:48.691274
Duration2.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

체육시설명
Text

Distinct267
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-14T17:44:49.536711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length9.8152174
Min length3

Characters and Unicode

Total characters2709
Distinct characters215
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

Unique258 ?
Unique (%)93.5%

Sample

1st row그린환경센터 동네체육시설
2nd row영천강변공원 동네체육시설
3rd row영천공설시장 고객건강증진센터
4th row완산 5통 동네체육시설
5th row완산10통 동네체육시설
ValueCountFrequency (%)
동네체육시설 67
 
11.7%
체육시설 57
 
10.0%
동네체육시설물 18
 
3.1%
경로당 12
 
2.1%
공원 11
 
1.9%
등산로 9
 
1.6%
소공원 8
 
1.4%
쉼터 8
 
1.4%
내 7
 
1.2%
마을회관 6
 
1.0%
Other values (311) 369
64.5%
2024-03-14T17:44:50.761580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
 
11.0%
설 160
 
5.9%
시 160
 
5.9%
리 158
 
5.8%
체 155
 
5.7%
육 154
 
5.7%
동 128
 
4.7%
네 92
 
3.4%
공 71
 
2.6%
원 65
 
2.4%
Other values (205) 1268
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2264
83.6%
Space Separator 298
 
11.0%
Decimal Number 125
 
4.6%
Open Punctuation 10
 
0.4%
Close Punctuation 10
 
0.4%
Uppercase Letter 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 160
 
7.1%
시 160
 
7.1%
리 158
 
7.0%
체 155
 
6.8%
육 154
 
6.8%
동 128
 
5.7%
네 92
 
4.1%
공 71
 
3.1%
원 65
 
2.9%
산 43
 
1.9%
Other values (194) 1078
47.6%
Decimal Number
ValueCountFrequency (%)
2 54
43.2%
1 52
41.6%
3 13
 
10.4%
5 3
 
2.4%
8 2
 
1.6%
0 1
 
0.8%
Space Separator
ValueCountFrequency (%)
298
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2264
83.6%
Common 444
 
16.4%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 160
 
7.1%
시 160
 
7.1%
리 158
 
7.0%
체 155
 
6.8%
육 154
 
6.8%
동 128
 
5.7%
네 92
 
4.1%
공 71
 
3.1%
원 65
 
2.9%
산 43
 
1.9%
Other values (194) 1078
47.6%
Common
ValueCountFrequency (%)
298
67.1%
2 54
 
12.2%
1 52
 
11.7%
3 13
 
2.9%
( 10
 
2.3%
) 10
 
2.3%
5 3
 
0.7%
8 2
 
0.5%
, 1
 
0.2%
0 1
 
0.2%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2264
83.6%
ASCII 445
 
16.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
67.0%
2 54
 
12.1%
1 52
 
11.7%
3 13
 
2.9%
( 10
 
2.2%
) 10
 
2.2%
5 3
 
0.7%
8 2
 
0.4%
A 1
 
0.2%
, 1
 
0.2%
Hangul
ValueCountFrequency (%)
설 160
 
7.1%
시 160
 
7.1%
리 158
 
7.0%
체 155
 
6.8%
육 154
 
6.8%
동 128
 
5.7%
네 92
 
4.1%
공 71
 
3.1%
원 65
 
2.9%
산 43
 
1.9%
Other values (194) 1078
47.6%

도로명주소
Text

Distinct173
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-14T17:44:52.045291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length33.5
Mean length16.797101
Min length3

Characters and Unicode

Total characters4636
Distinct characters179
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

Unique163 ?
Unique (%)59.1%

Sample

1st row경상북도 영천시 완산동
2nd row경북 영천시 완산동 1048-3
3rd row경상북도 영천시 완산동
4th row경상북도 영천시 완산동
5th row경상북도 영천시 완산동
ValueCountFrequency (%)
영천시 190
19.9%
경상북도 152
 
15.9%
경북 38
 
4.0%
임고면 19
 
2.0%
금호읍 18
 
1.9%
고경면 16
 
1.7%
화북면 14
 
1.5%
화산면 14
 
1.5%
대창면 13
 
1.4%
청통면 11
 
1.2%
Other values (350) 470
49.2%
2024-03-14T17:44:53.743802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1359
29.3%
경 213
 
4.6%
북 212
 
4.6%
천 210
 
4.5%
영 194
 
4.2%
시 191
 
4.1%
도 163
 
3.5%
상 156
 
3.4%
1 141
 
3.0%
면 114
 
2.5%
Other values (169) 1683
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2504
54.0%
Space Separator 1359
29.3%
Decimal Number 605
 
13.1%
Dash Punctuation 73
 
1.6%
Open Punctuation 41
 
0.9%
Close Punctuation 41
 
0.9%
Other Punctuation 13
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 213
 
8.5%
북 212
 
8.5%
천 210
 
8.4%
영 194
 
7.7%
시 191
 
7.6%
도 163
 
6.5%
상 156
 
6.2%
면 114
 
4.6%
길 109
 
4.4%
동 69
 
2.8%
Other values (154) 873
34.9%
Decimal Number
ValueCountFrequency (%)
1 141
23.3%
2 84
13.9%
4 68
11.2%
3 60
9.9%
6 48
 
7.9%
7 46
 
7.6%
5 43
 
7.1%
0 43
 
7.1%
9 37
 
6.1%
8 35
 
5.8%
Space Separator
ValueCountFrequency (%)
1359
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2504
54.0%
Common 2132
46.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 213
 
8.5%
북 212
 
8.5%
천 210
 
8.4%
영 194
 
7.7%
시 191
 
7.6%
도 163
 
6.5%
상 156
 
6.2%
면 114
 
4.6%
길 109
 
4.4%
동 69
 
2.8%
Other values (154) 873
34.9%
Common
ValueCountFrequency (%)
1359
63.7%
1 141
 
6.6%
2 84
 
3.9%
- 73
 
3.4%
4 68
 
3.2%
3 60
 
2.8%
6 48
 
2.3%
7 46
 
2.2%
5 43
 
2.0%
0 43
 
2.0%
Other values (5) 167
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2504
54.0%
ASCII 2132
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1359
63.7%
1 141
 
6.6%
2 84
 
3.9%
- 73
 
3.4%
4 68
 
3.2%
3 60
 
2.8%
6 48
 
2.3%
7 46
 
2.2%
5 43
 
2.0%
0 43
 
2.0%
Other values (5) 167
 
7.8%
Hangul
ValueCountFrequency (%)
경 213
 
8.5%
북 212
 
8.5%
천 210
 
8.4%
영 194
 
7.7%
시 191
 
7.6%
도 163
 
6.5%
상 156
 
6.2%
면 114
 
4.6%
길 109
 
4.4%
동 69
 
2.8%
Other values (154) 873
34.9%

지번주소
Text

Distinct244
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-14T17:44:54.702103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length38
Mean length22.898551
Min length3

Characters and Unicode

Total characters6320
Distinct characters168
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

Unique234 ?
Unique (%)84.8%

Sample

1st row경상북도 영천시 완산동 81
2nd row경북 영천시 완산동 1048-3
3rd row경상북도 영천시 완산동 982-3
4th row경상북도 영천시 완산동 1269-38
5th row경상북도 영천시 완산동 1090-13
ValueCountFrequency (%)
영천시 275
 
19.7%
경상북도 180
 
12.9%
경북 95
 
6.8%
임고면 27
 
1.9%
북안면 23
 
1.6%
금호읍 23
 
1.6%
고경면 21
 
1.5%
화산면 20
 
1.4%
대창면 16
 
1.1%
야사동 16
 
1.1%
Other values (466) 698
50.1%
2024-03-14T17:44:55.890105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1541
24.4%
경 322
 
5.1%
북 313
 
5.0%
천 304
 
4.8%
영 276
 
4.4%
시 275
 
4.4%
1 227
 
3.6%
리 207
 
3.3%
도 193
 
3.1%
상 187
 
3.0%
Other values (158) 2475
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3564
56.4%
Space Separator 1541
24.4%
Decimal Number 1033
 
16.3%
Dash Punctuation 160
 
2.5%
Close Punctuation 11
 
0.2%
Open Punctuation 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 322
 
9.0%
북 313
 
8.8%
천 304
 
8.5%
영 276
 
7.7%
시 275
 
7.7%
리 207
 
5.8%
도 193
 
5.4%
상 187
 
5.2%
면 166
 
4.7%
동 97
 
2.7%
Other values (144) 1224
34.3%
Decimal Number
ValueCountFrequency (%)
1 227
22.0%
2 123
11.9%
4 120
11.6%
3 104
10.1%
7 91
8.8%
5 87
 
8.4%
6 78
 
7.6%
9 71
 
6.9%
8 70
 
6.8%
0 62
 
6.0%
Space Separator
ValueCountFrequency (%)
1541
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 160
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3564
56.4%
Common 2756
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 322
 
9.0%
북 313
 
8.8%
천 304
 
8.5%
영 276
 
7.7%
시 275
 
7.7%
리 207
 
5.8%
도 193
 
5.4%
상 187
 
5.2%
면 166
 
4.7%
동 97
 
2.7%
Other values (144) 1224
34.3%
Common
ValueCountFrequency (%)
1541
55.9%
1 227
 
8.2%
- 160
 
5.8%
2 123
 
4.5%
4 120
 
4.4%
3 104
 
3.8%
7 91
 
3.3%
5 87
 
3.2%
6 78
 
2.8%
9 71
 
2.6%
Other values (4) 154
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3564
56.4%
ASCII 2756
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1541
55.9%
1 227
 
8.2%
- 160
 
5.8%
2 123
 
4.5%
4 120
 
4.4%
3 104
 
3.8%
7 91
 
3.3%
5 87
 
3.2%
6 78
 
2.8%
9 71
 
2.6%
Other values (4) 154
 
5.6%
Hangul
ValueCountFrequency (%)
경 322
 
9.0%
북 313
 
8.8%
천 304
 
8.5%
영 276
 
7.7%
시 275
 
7.7%
리 207
 
5.8%
도 193
 
5.4%
상 187
 
5.2%
면 166
 
4.7%
동 97
 
2.7%
Other values (144) 1224
34.3%

위도
Real number (ℝ)

Distinct240
Distinct (%)87.3%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean35.985388
Minimum35.848451
Maximum36.144709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-14T17:44:56.130596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.848451
5-th percentile35.882452
Q135.941775
median35.978689
Q336.030586
95-th percentile36.104566
Maximum36.144709
Range0.29625833
Interquartile range (IQR)0.088810725

Descriptive statistics

Standard deviation0.063554662
Coefficient of variation (CV)0.0017661241
Kurtosis-0.28216668
Mean35.985388
Median Absolute Deviation (MAD)0.04576669
Skewness0.18337554
Sum9895.9816
Variance0.004039195
MonotonicityNot monotonic
2024-03-14T17:44:56.576917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.90276705 11
 
4.0%
36.03058596 8
 
2.9%
36.10456555 6
 
2.2%
35.92284105 5
 
1.8%
36.10806124 3
 
1.1%
35.92886137 2
 
0.7%
35.98429022 2
 
0.7%
35.94173119 2
 
0.7%
35.98693486 2
 
0.7%
35.96089617 2
 
0.7%
Other values (230) 232
84.1%
ValueCountFrequency (%)
35.84845104 1
0.4%
35.85000942 1
0.4%
35.85494068 1
0.4%
35.85658388 1
0.4%
35.85753425 1
0.4%
35.86118404 1
0.4%
35.86187807 1
0.4%
35.8630517 1
0.4%
35.86377715 1
0.4%
35.86953142 1
0.4%
ValueCountFrequency (%)
36.14470937 1
 
0.4%
36.14427999 1
 
0.4%
36.13529339 1
 
0.4%
36.13295118 1
 
0.4%
36.13033063 1
 
0.4%
36.11356734 1
 
0.4%
36.10914906 1
 
0.4%
36.10806124 3
1.1%
36.10767905 1
 
0.4%
36.10566253 1
 
0.4%

경도
Real number (ℝ)

Distinct240
Distinct (%)87.3%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean128.93697
Minimum128.7122
Maximum129.1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-14T17:44:56.995411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.7122
5-th percentile128.81232
Q1128.89525
median128.93613
Q3128.98351
95-th percentile129.06292
Maximum129.1124
Range0.4001987
Interquartile range (IQR)0.08826045

Descriptive statistics

Standard deviation0.073150387
Coefficient of variation (CV)0.00056733449
Kurtosis0.62910217
Mean128.93697
Median Absolute Deviation (MAD)0.041639
Skewness-0.16837404
Sum35457.665
Variance0.0053509791
MonotonicityNot monotonic
2024-03-14T17:44:57.463116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0018994 11
 
4.0%
128.8568423 8
 
2.9%
129.0478397 6
 
2.2%
128.8944927 5
 
1.8%
128.9197488 3
 
1.1%
128.9077255 2
 
0.7%
128.9575643 2
 
0.7%
128.9313032 2
 
0.7%
128.9155664 2
 
0.7%
129.1107219 2
 
0.7%
Other values (230) 232
84.1%
ValueCountFrequency (%)
128.7121985 1
0.4%
128.7128203 1
0.4%
128.7207575 1
0.4%
128.7372913 1
0.4%
128.7447935 1
0.4%
128.7813181 1
0.4%
128.7833721 1
0.4%
128.7881562 1
0.4%
128.7884245 1
0.4%
128.8009076 1
0.4%
ValueCountFrequency (%)
129.1123972 1
0.4%
129.1107219 2
0.7%
129.1074105 1
0.4%
129.1046403 1
0.4%
129.1018141 1
0.4%
129.0940797 1
0.4%
129.0935738 1
0.4%
129.0918653 1
0.4%
129.0812709 1
0.4%
129.0800587 1
0.4%

Interactions

2024-03-14T17:44:47.349436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:44:46.811884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:44:47.622667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:44:47.071376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T17:44:57.741207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.729
경도0.7291.000
2024-03-14T17:44:57.971610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.000-0.056
경도-0.0561.000

Missing values

2024-03-14T17:44:47.987154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T17:44:48.296473image/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.
2024-03-14T17:44:48.567116image/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

체육시설명도로명주소지번주소위도경도
0그린환경센터 동네체육시설경상북도 영천시 완산동경상북도 영천시 완산동 8135.962896128.963991
1영천강변공원 동네체육시설경북 영천시 완산동 1048-3경북 영천시 완산동 1048-335.965965128.935025
2영천공설시장 고객건강증진센터경상북도 영천시 완산동경상북도 영천시 완산동 982-335.964252128.938586
3완산 5통 동네체육시설경상북도 영천시 완산동경상북도 영천시 완산동 1269-3835.964857128.936132
4완산10통 동네체육시설경상북도 영천시 완산동경상북도 영천시 완산동 1090-1335.961918128.934988
5완산3통 동네체육시설물경상북도 영천시 완산2길 24(완산동) 완산3통 경로당경상북도 영천시 완산동 966-17 완산3통 경로당35.964943128.940013
6완산8통 동네체육시설경북 영천시 완산동 907-235.962181128.938328
7은하맨션 동네체육시설물경북 영천시 완산동 939-21경북 영천시 완산동 939-2135.960071128.940731
8도남동 야외운동기구경상북도 영천시 도남공단3길 96(도남동) 도남동 182경상북도 영천시 도남동 193 화신정공 도남동 18235.931577128.941224
9고지2리 동네체육시설경북 영천시 북안면 고지리 86835.909604129.02154
체육시설명도로명주소지번주소위도경도
266연계1리 동네체육시설경상북도 영천시 화산면36.030586128.856842
267연계1리 연계쉼터경상북도 영천시 화산면36.030586128.856842
268용산리 소공원 체육시설경상북도 영천시 자양면36.104566129.04784
269유성1리 동네체육시설경상북도 영천시 화산면36.030586128.856842
270임포2리 경로당 2층 건강관리실경상북도 영천시 북안면35.902767129.001899
271임포교회 교육관 앞 체육시설경상북도 영천시 북안면35.902767129.001899
272충효3리 소공원 체육시설경상북도 영천시 자양면36.104566129.04784
273화산면 게이트볼장경상북도 영천시 화산면36.030586128.856842
274화산면 복지회관경상북도 영천시 화산면36.030586128.856842
275효정1리 동네체육시설경상북도 영천시 화산면36.030586128.856842

Duplicate rows

Most frequently occurring

체육시설명도로명주소지번주소위도경도# duplicates
0대전 1동 마을회관경상북도 영천시 대전길 121(대전동)경상북도 영천시 대전동 365-135.986935128.9155662
1문외공원경상북도 영천시 청산1길 19 (문외동)경상북도 영천시 문외동 5-435.974107128.9368842
2우로지 자연생태공원 체육시설경북 영천시 망정동 432경북 영천시 망정동 43235.98429128.9575642
3파계리 동네체육시설경상북도 영천시 고경면 용담로 1809경상북도 영천시 고경면 파계리 239 파계리경로당35.960896129.1107222