Overview

Dataset statistics

Number of variables15
Number of observations33
Missing cells6
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory130.0 B

Variable types

Numeric6
Categorical3
Text5
DateTime1

Dataset

Description대전광역시 서구 관내에 있는 지진옥외대피장소(시설구분, 시설명, 지번주소, 도로명주소, 수용가능면적 등)에 대한 정보를 제공합니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15124341/fileData.do

Alerts

수용가능면적단위 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 수용가능면적High correlation
행정동코드 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
수용가능면적 is highly overall correlated with 연번High correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동명 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
도로명주소 has 4 (12.1%) missing valuesMissing
위도 has 1 (3.0%) missing valuesMissing
경도 has 1 (3.0%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique
지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:07:28.942492
Analysis finished2023-12-12 06:07:34.040973
Duration5.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T15:07:34.107358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q19
median17
Q325
95-th percentile31.4
Maximum33
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.6695398
Coefficient of variation (CV)0.56879646
Kurtosis-1.2
Mean17
Median Absolute Deviation (MAD)8
Skewness0
Sum561
Variance93.5
MonotonicityNot monotonic
2023-12-12T15:07:34.256059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 1
 
3.0%
29 1
 
3.0%
22 1
 
3.0%
23 1
 
3.0%
24 1
 
3.0%
25 1
 
3.0%
26 1
 
3.0%
27 1
 
3.0%
8 1
 
3.0%
2 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1 1
3.0%
2 1
3.0%
3 1
3.0%
4 1
3.0%
5 1
3.0%
6 1
3.0%
7 1
3.0%
8 1
3.0%
9 1
3.0%
10 1
3.0%
ValueCountFrequency (%)
33 1
3.0%
32 1
3.0%
31 1
3.0%
30 1
3.0%
29 1
3.0%
28 1
3.0%
27 1
3.0%
26 1
3.0%
25 1
3.0%
24 1
3.0%

시설구분명
Categorical

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
학교운동장
28 
주차장(옥외)
5 

Length

Max length7
Median length5
Mean length5.3030303
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
학교운동장 28
84.8%
주차장(옥외) 5
 
15.2%

Length

2023-12-12T15:07:34.422249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:07:34.558063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
학교운동장 28
84.8%
주차장(옥외 5
 
15.2%

시설명
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T15:07:34.829823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length9.7878788
Min length8

Characters and Unicode

Total characters323
Distinct characters67
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

Unique33 ?
Unique (%)100.0%

Sample

1st row만년초등학교운동장
2nd row탄방중학교운동장
3rd row기성중학교운동장
4th row선유초등학교운동장
5th row구봉초등학교운동장
ValueCountFrequency (%)
만년초등학교운동장 1
 
2.8%
탄방중학교운동장 1
 
2.8%
대전과학기술대학교운동장 1
 
2.8%
건양대학교 1
 
2.8%
대전메디컬 1
 
2.8%
캠퍼스 1
 
2.8%
주차장 1
 
2.8%
대전원앙초등학교운동장 1
 
2.8%
대전선암초등학교운동장 1
 
2.8%
대전금동초등학교운동장 1
 
2.8%
Other values (26) 26
72.2%
2023-12-12T15:07:35.280236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 36
 
11.1%
장 34
 
10.5%
학 30
 
9.3%
교 29
 
9.0%
운 29
 
9.0%
초 17
 
5.3%
등 17
 
5.3%
대 15
 
4.6%
전 11
 
3.4%
중 8
 
2.5%
Other values (57) 97
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 316
97.8%
Space Separator 3
 
0.9%
Decimal Number 3
 
0.9%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 36
 
11.4%
장 34
 
10.8%
학 30
 
9.5%
교 29
 
9.2%
운 29
 
9.2%
초 17
 
5.4%
등 17
 
5.4%
대 15
 
4.7%
전 11
 
3.5%
중 8
 
2.5%
Other values (53) 90
28.5%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 316
97.8%
Common 7
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 36
 
11.4%
장 34
 
10.8%
학 30
 
9.5%
교 29
 
9.2%
운 29
 
9.2%
초 17
 
5.4%
등 17
 
5.4%
대 15
 
4.7%
전 11
 
3.5%
중 8
 
2.5%
Other values (53) 90
28.5%
Common
ValueCountFrequency (%)
3
42.9%
2 2
28.6%
' 1
 
14.3%
3 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 316
97.8%
ASCII 7
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 36
 
11.4%
장 34
 
10.8%
학 30
 
9.5%
교 29
 
9.2%
운 29
 
9.2%
초 17
 
5.4%
등 17
 
5.4%
대 15
 
4.7%
전 11
 
3.5%
중 8
 
2.5%
Other values (53) 90
28.5%
ASCII
ValueCountFrequency (%)
3
42.9%
2 2
28.6%
' 1
 
14.3%
3 1
 
14.3%

지번주소
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T15:07:35.600158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length23.181818
Min length16

Characters and Unicode

Total characters765
Distinct characters70
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

Unique33 ?
Unique (%)100.0%

Sample

1st row대전광역시 서구 만년동 280 대전만년초등교
2nd row대전광역시 서구 둔산동 1512 탄방중학교
3rd row대전광역시 서구 흑석동 295 기성중학교
4th row대전광역시 서구 관저동 1592 대전선유초등학교
5th row대전광역시 서구 관저동 1133 대전구봉초등학교
ValueCountFrequency (%)
대전광역시 33
20.8%
서구 33
20.8%
관저동 7
 
4.4%
도마동 4
 
2.5%
도안동 3
 
1.9%
복수동 2
 
1.3%
둔산동 2
 
1.3%
월평동 2
 
1.3%
괴정동 2
 
1.3%
변동 2
 
1.3%
Other values (68) 69
43.4%
2023-12-12T15:07:36.068305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
126
16.5%
대 48
 
6.3%
전 45
 
5.9%
동 37
 
4.8%
1 36
 
4.7%
구 34
 
4.4%
역 33
 
4.3%
광 33
 
4.3%
서 33
 
4.3%
시 33
 
4.3%
Other values (60) 307
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 507
66.3%
Space Separator 126
 
16.5%
Decimal Number 118
 
15.4%
Dash Punctuation 14
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 48
 
9.5%
전 45
 
8.9%
동 37
 
7.3%
구 34
 
6.7%
역 33
 
6.5%
광 33
 
6.5%
서 33
 
6.5%
시 33
 
6.5%
교 27
 
5.3%
학 27
 
5.3%
Other values (48) 157
31.0%
Decimal Number
ValueCountFrequency (%)
1 36
30.5%
3 17
14.4%
4 12
 
10.2%
2 10
 
8.5%
5 9
 
7.6%
8 9
 
7.6%
0 8
 
6.8%
9 7
 
5.9%
6 7
 
5.9%
7 3
 
2.5%
Space Separator
ValueCountFrequency (%)
126
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 507
66.3%
Common 258
33.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 48
 
9.5%
전 45
 
8.9%
동 37
 
7.3%
구 34
 
6.7%
역 33
 
6.5%
광 33
 
6.5%
서 33
 
6.5%
시 33
 
6.5%
교 27
 
5.3%
학 27
 
5.3%
Other values (48) 157
31.0%
Common
ValueCountFrequency (%)
126
48.8%
1 36
 
14.0%
3 17
 
6.6%
- 14
 
5.4%
4 12
 
4.7%
2 10
 
3.9%
5 9
 
3.5%
8 9
 
3.5%
0 8
 
3.1%
9 7
 
2.7%
Other values (2) 10
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 507
66.3%
ASCII 258
33.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
126
48.8%
1 36
 
14.0%
3 17
 
6.6%
- 14
 
5.4%
4 12
 
4.7%
2 10
 
3.9%
5 9
 
3.5%
8 9
 
3.5%
0 8
 
3.1%
9 7
 
2.7%
Other values (2) 10
 
3.9%
Hangul
ValueCountFrequency (%)
대 48
 
9.5%
전 45
 
8.9%
동 37
 
7.3%
구 34
 
6.7%
역 33
 
6.5%
광 33
 
6.5%
서 33
 
6.5%
시 33
 
6.5%
교 27
 
5.3%
학 27
 
5.3%
Other values (48) 157
31.0%

도로명주소
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing4
Missing (%)12.1%
Memory size396.0 B
2023-12-12T15:07:36.356562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length21.724138
Min length20

Characters and Unicode

Total characters630
Distinct characters68
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

Unique29 ?
Unique (%)100.0%

Sample

1st row대전광역시 서구 만년남로 14(만년동)
2nd row대전광역시 서구 문정로 181(둔산동)
3rd row대전광역시 서구 장안로 54(흑석동)
4th row대전광역시 서구 관저남로 60(관저동)
5th row대전광역시 서구 관저로 75(관저동)
ValueCountFrequency (%)
대전광역시 29
25.0%
서구 29
25.0%
관저로 2
 
1.7%
원도안로 2
 
1.7%
문정로 2
 
1.7%
54(흑석동 1
 
0.9%
관저남로 1
 
0.9%
29(도마동 1
 
0.9%
혜천로 1
 
0.9%
100(복수동 1
 
0.9%
Other values (47) 47
40.5%
2023-12-12T15:07:36.830896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
 
13.8%
동 32
 
5.1%
서 31
 
4.9%
대 30
 
4.8%
시 30
 
4.8%
( 29
 
4.6%
) 29
 
4.6%
전 29
 
4.6%
구 29
 
4.6%
역 29
 
4.6%
Other values (58) 275
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 399
63.3%
Space Separator 87
 
13.8%
Decimal Number 85
 
13.5%
Open Punctuation 29
 
4.6%
Close Punctuation 29
 
4.6%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 32
 
8.0%
서 31
 
7.8%
대 30
 
7.5%
시 30
 
7.5%
전 29
 
7.3%
구 29
 
7.3%
역 29
 
7.3%
광 29
 
7.3%
로 26
 
6.5%
도 13
 
3.3%
Other values (44) 121
30.3%
Decimal Number
ValueCountFrequency (%)
1 19
22.4%
6 11
12.9%
5 10
11.8%
2 10
11.8%
0 9
10.6%
3 7
 
8.2%
7 6
 
7.1%
9 5
 
5.9%
4 4
 
4.7%
8 4
 
4.7%
Space Separator
ValueCountFrequency (%)
87
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 399
63.3%
Common 231
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 32
 
8.0%
서 31
 
7.8%
대 30
 
7.5%
시 30
 
7.5%
전 29
 
7.3%
구 29
 
7.3%
역 29
 
7.3%
광 29
 
7.3%
로 26
 
6.5%
도 13
 
3.3%
Other values (44) 121
30.3%
Common
ValueCountFrequency (%)
87
37.7%
( 29
 
12.6%
) 29
 
12.6%
1 19
 
8.2%
6 11
 
4.8%
5 10
 
4.3%
2 10
 
4.3%
0 9
 
3.9%
3 7
 
3.0%
7 6
 
2.6%
Other values (4) 14
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 399
63.3%
ASCII 231
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
87
37.7%
( 29
 
12.6%
) 29
 
12.6%
1 19
 
8.2%
6 11
 
4.8%
5 10
 
4.3%
2 10
 
4.3%
0 9
 
3.9%
3 7
 
3.0%
7 6
 
2.6%
Other values (4) 14
 
6.1%
Hangul
ValueCountFrequency (%)
동 32
 
8.0%
서 31
 
7.8%
대 30
 
7.5%
시 30
 
7.5%
전 29
 
7.3%
구 29
 
7.3%
역 29
 
7.3%
광 29
 
7.3%
로 26
 
6.5%
도 13
 
3.3%
Other values (44) 121
30.3%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170575 × 109
Minimum3.017051 × 109
Maximum3.017065 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T15:07:37.016879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017051 × 109
5-th percentile3.0170516 × 109
Q13.017054 × 109
median3.0170588 × 109
Q33.0170597 × 109
95-th percentile3.0170634 × 109
Maximum3.017065 × 109
Range14000
Interquartile range (IQR)5700

Descriptive statistics

Standard deviation3581.1084
Coefficient of variation (CV)1.186954 × 10-6
Kurtosis-0.45893751
Mean3.0170575 × 109
Median Absolute Deviation (MAD)1300
Skewness-0.13234456
Sum9.9562898 × 1010
Variance12824337
MonotonicityNot monotonic
2023-12-12T15:07:37.170986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3017059700 5
15.2%
3017059300 3
 
9.1%
3017053000 3
 
9.1%
3017051000 2
 
6.1%
3017059000 2
 
6.1%
3017056000 2
 
6.1%
3017054000 2
 
6.1%
3017059600 2
 
6.1%
3017065000 1
 
3.0%
3017057500 1
 
3.0%
Other values (10) 10
30.3%
ValueCountFrequency (%)
3017051000 2
6.1%
3017052000 1
 
3.0%
3017053000 3
9.1%
3017053500 1
 
3.0%
3017054000 2
6.1%
3017055500 1
 
3.0%
3017056000 2
6.1%
3017057000 1
 
3.0%
3017057500 1
 
3.0%
3017058200 1
 
3.0%
ValueCountFrequency (%)
3017065000 1
 
3.0%
3017064000 1
 
3.0%
3017063000 1
 
3.0%
3017060000 1
 
3.0%
3017059700 5
15.2%
3017059600 2
 
6.1%
3017059300 3
9.1%
3017059000 2
 
6.1%
3017058800 1
 
3.0%
3017058600 1
 
3.0%

행정동명
Text

Distinct20
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T15:07:37.387969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4545455
Min length2

Characters and Unicode

Total characters114
Distinct characters30
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)36.4%

Sample

1st row만년동
2nd row둔산1동
3rd row기성동
4th row관저2동
5th row관저2동
ValueCountFrequency (%)
관저2동 5
15.2%
도안동 3
 
9.1%
도마2동 3
 
9.1%
복수동 2
 
6.1%
가수원동 2
 
6.1%
괴정동 2
 
6.1%
변동 2
 
6.1%
관저1동 2
 
6.1%
도마1동 1
 
3.0%
만년동 1
 
3.0%
Other values (10) 10
30.3%
2023-12-12T15:07:37.806559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 33
28.9%
2 10
 
8.8%
관 7
 
6.1%
도 7
 
6.1%
저 7
 
6.1%
1 5
 
4.4%
마 5
 
4.4%
수 4
 
3.5%
안 3
 
2.6%
가 3
 
2.6%
Other values (20) 30
26.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 98
86.0%
Decimal Number 16
 
14.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 33
33.7%
관 7
 
7.1%
도 7
 
7.1%
저 7
 
7.1%
마 5
 
5.1%
수 4
 
4.1%
안 3
 
3.1%
가 3
 
3.1%
정 3
 
3.1%
평 2
 
2.0%
Other values (17) 24
24.5%
Decimal Number
ValueCountFrequency (%)
2 10
62.5%
1 5
31.2%
3 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 98
86.0%
Common 16
 
14.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 33
33.7%
관 7
 
7.1%
도 7
 
7.1%
저 7
 
7.1%
마 5
 
5.1%
수 4
 
4.1%
안 3
 
3.1%
가 3
 
3.1%
정 3
 
3.1%
평 2
 
2.0%
Other values (17) 24
24.5%
Common
ValueCountFrequency (%)
2 10
62.5%
1 5
31.2%
3 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 98
86.0%
ASCII 16
 
14.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 33
33.7%
관 7
 
7.1%
도 7
 
7.1%
저 7
 
7.1%
마 5
 
5.1%
수 4
 
4.1%
안 3
 
3.1%
가 3
 
3.1%
정 3
 
3.1%
평 2
 
2.0%
Other values (17) 24
24.5%
ASCII
ValueCountFrequency (%)
2 10
62.5%
1 5
31.2%
3 1
 
6.2%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170111 × 109
Minimum3.0170101 × 109
Maximum3.0170128 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T15:07:37.951391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0170101 × 109
5-th percentile3.0170102 × 109
Q13.0170104 × 109
median3.0170112 × 109
Q33.0170116 × 109
95-th percentile3.0170116 × 109
Maximum3.0170128 × 109
Range2700
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation634.5632
Coefficient of variation (CV)2.1032843 × 10-7
Kurtosis0.037961718
Mean3.0170111 × 109
Median Absolute Deviation (MAD)400
Skewness0.16741573
Sum9.9561365 × 1010
Variance402670.45
MonotonicityNot monotonic
2023-12-12T15:07:38.107136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3017011600 7
21.2%
3017010300 4
12.1%
3017011500 3
9.1%
3017010100 2
 
6.1%
3017011400 2
 
6.1%
3017011300 2
 
6.1%
3017010800 2
 
6.1%
3017011200 2
 
6.1%
3017010200 2
 
6.1%
3017012800 1
 
3.0%
Other values (6) 6
18.2%
ValueCountFrequency (%)
3017010100 2
6.1%
3017010200 2
6.1%
3017010300 4
12.1%
3017010400 1
 
3.0%
3017010600 1
 
3.0%
3017010800 2
6.1%
3017010900 1
 
3.0%
3017011000 1
 
3.0%
3017011100 1
 
3.0%
3017011200 2
6.1%
ValueCountFrequency (%)
3017012800 1
 
3.0%
3017011700 1
 
3.0%
3017011600 7
21.2%
3017011500 3
9.1%
3017011400 2
 
6.1%
3017011300 2
 
6.1%
3017011200 2
 
6.1%
3017011100 1
 
3.0%
3017011000 1
 
3.0%
3017010900 1
 
3.0%

법정동명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Memory size396.0 B
관저동
7 
도마동
4 
도안동
3 
둔산동
2 
가수원동
2 
Other values (11)
15 

Length

Max length4
Median length3
Mean length2.969697
Min length2

Unique

Unique7 ?
Unique (%)21.2%

Sample

1st row만년동
2nd row둔산동
3rd row흑석동
4th row관저동
5th row관저동

Common Values

ValueCountFrequency (%)
관저동 7
21.2%
도마동 4
12.1%
도안동 3
9.1%
둔산동 2
 
6.1%
가수원동 2
 
6.1%
월평동 2
 
6.1%
괴정동 2
 
6.1%
변동 2
 
6.1%
복수동 2
 
6.1%
만년동 1
 
3.0%
Other values (6) 6
18.2%

Length

2023-12-12T15:07:38.299529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
관저동 7
21.2%
도마동 4
12.1%
도안동 3
9.1%
둔산동 2
 
6.1%
가수원동 2
 
6.1%
월평동 2
 
6.1%
괴정동 2
 
6.1%
변동 2
 
6.1%
복수동 2
 
6.1%
만년동 1
 
3.0%
Other values (6) 6
18.2%

수용가능면적
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5032.203
Minimum243
Maximum19150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T15:07:38.424071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum243
5-th percentile463.6
Q1987
median3687
Q37306
95-th percentile14184
Maximum19150
Range18907
Interquartile range (IQR)6319

Descriptive statistics

Standard deviation4543.9207
Coefficient of variation (CV)0.90296848
Kurtosis2.0355444
Mean5032.203
Median Absolute Deviation (MAD)2769
Skewness1.4050708
Sum166062.7
Variance20647216
MonotonicityNot monotonic
2023-12-12T15:07:38.580080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
8088.0 2
 
6.1%
4180.0 1
 
3.0%
850.0 1
 
3.0%
19150.0 1
 
3.0%
2100.0 1
 
3.0%
584.0 1
 
3.0%
283.0 1
 
3.0%
243.0 1
 
3.0%
2913.0 1
 
3.0%
6215.0 1
 
3.0%
Other values (22) 22
66.7%
ValueCountFrequency (%)
243.0 1
3.0%
283.0 1
3.0%
584.0 1
3.0%
592.0 1
3.0%
707.0 1
3.0%
779.0 1
3.0%
850.0 1
3.0%
918.0 1
3.0%
987.0 1
3.0%
2100.0 1
3.0%
ValueCountFrequency (%)
19150.0 1
3.0%
15147.0 1
3.0%
13542.0 1
3.0%
10000.0 1
3.0%
9914.0 1
3.0%
8088.0 2
6.1%
7468.0 1
3.0%
7306.0 1
3.0%
6400.0 1
3.0%
6215.0 1
3.0%

수용가능면적단위
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
제곱미터
33 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제곱미터 33
100.0%

Length

2023-12-12T15:07:38.720321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:07:38.808760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제곱미터 33
100.0%

연락처
Text

Distinct31
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T15:07:39.004051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters396
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

Unique30 ?
Unique (%)90.9%

Sample

1st row042-485-5765
2nd row042-380-3101
3rd row042-610-0200
4th row042-540-5183
5th row042-542-0193
ValueCountFrequency (%)
042-288-4125 3
 
9.1%
042-485-5765 1
 
3.0%
042-525-5660 1
 
3.0%
042-586-5869 1
 
3.0%
042-535-8791 1
 
3.0%
042-530-3206 1
 
3.0%
042-483-6733 1
 
3.0%
042-543-7803 1
 
3.0%
042-333-2162 1
 
3.0%
042-488-3323 1
 
3.0%
Other values (21) 21
63.6%
2023-12-12T15:07:39.313823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 66
16.7%
2 64
16.2%
4 59
14.9%
0 55
13.9%
5 36
9.1%
3 32
8.1%
8 28
7.1%
6 20
 
5.1%
1 19
 
4.8%
7 11
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
83.3%
Dash Punctuation 66
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 64
19.4%
4 59
17.9%
0 55
16.7%
5 36
10.9%
3 32
9.7%
8 28
8.5%
6 20
 
6.1%
1 19
 
5.8%
7 11
 
3.3%
9 6
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 66
16.7%
2 64
16.2%
4 59
14.9%
0 55
13.9%
5 36
9.1%
3 32
8.1%
8 28
7.1%
6 20
 
5.1%
1 19
 
4.8%
7 11
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 66
16.7%
2 64
16.2%
4 59
14.9%
0 55
13.9%
5 36
9.1%
3 32
8.1%
8 28
7.1%
6 20
 
5.1%
1 19
 
4.8%
7 11
 
2.8%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing1
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean36.320805
Minimum36.250011
Maximum36.366768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T15:07:39.456056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.250011
5-th percentile36.296077
Q136.30261
median36.319571
Q336.336597
95-th percentile36.359323
Maximum36.366768
Range0.11675717
Interquartile range (IQR)0.033987365

Descriptive statistics

Standard deviation0.024466804
Coefficient of variation (CV)0.00067363055
Kurtosis0.96474178
Mean36.320805
Median Absolute Deviation (MAD)0.017025685
Skewness-0.29704843
Sum1162.2658
Variance0.00059862449
MonotonicityNot monotonic
2023-12-12T15:07:39.594771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
36.36676812 1
 
3.0%
36.31863227 1
 
3.0%
36.32460021 1
 
3.0%
36.3017064 1
 
3.0%
36.33084605 1
 
3.0%
36.34243608 1
 
3.0%
36.35833209 1
 
3.0%
36.32150849 1
 
3.0%
36.31781032 1
 
3.0%
36.36053489 1
 
3.0%
Other values (22) 22
66.7%
ValueCountFrequency (%)
36.25001095 1
3.0%
36.29460229 1
3.0%
36.29728334 1
3.0%
36.2980968 1
3.0%
36.29816054 1
3.0%
36.2995184 1
3.0%
36.3017064 1
3.0%
36.30241692 1
3.0%
36.30267415 1
3.0%
36.30501661 1
3.0%
ValueCountFrequency (%)
36.36676812 1
3.0%
36.36053489 1
3.0%
36.35833209 1
3.0%
36.35581314 1
3.0%
36.35067872 1
3.0%
36.34552367 1
3.0%
36.34243608 1
3.0%
36.34056272 1
3.0%
36.33527537 1
3.0%
36.33084605 1
3.0%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing1
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean127.36456
Minimum127.32627
Maximum127.39438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T15:07:39.774579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.32627
5-th percentile127.33219
Q1127.3444
median127.37025
Q3127.3799
95-th percentile127.39247
Maximum127.39438
Range0.0681098
Interquartile range (IQR)0.035504325

Descriptive statistics

Standard deviation0.020715456
Coefficient of variation (CV)0.00016264694
Kurtosis-1.2454532
Mean127.36456
Median Absolute Deviation (MAD)0.0154239
Skewness-0.33468733
Sum4075.6659
Variance0.00042913012
MonotonicityNot monotonic
2023-12-12T15:07:39.919548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
127.3773807 1
 
3.0%
127.3721112 1
 
3.0%
127.338786 1
 
3.0%
127.3780492 1
 
3.0%
127.3790208 1
 
3.0%
127.3767425 1
 
3.0%
127.3943644 1
 
3.0%
127.344635 1
 
3.0%
127.345788 1
 
3.0%
127.3676964 1
 
3.0%
Other values (22) 22
66.7%
ValueCountFrequency (%)
127.3262729 1
3.0%
127.3299677 1
3.0%
127.3340022 1
3.0%
127.3368772 1
3.0%
127.338786 1
3.0%
127.3411633 1
3.0%
127.3413916 1
3.0%
127.3436906 1
3.0%
127.344635 1
3.0%
127.345788 1
3.0%
ValueCountFrequency (%)
127.3943827 1
3.0%
127.3943644 1
3.0%
127.3909167 1
3.0%
127.3884971 1
3.0%
127.3854877 1
3.0%
127.384886 1
3.0%
127.3833884 1
3.0%
127.3825505 1
3.0%
127.3790208 1
3.0%
127.3780492 1
3.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
Minimum2022-09-23 00:00:00
Maximum2022-09-23 00:00:00
2023-12-12T15:07:40.042729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:40.140291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T15:07:32.579581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:29.588760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.192249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.812795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.469115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.055354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.697348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:29.695919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.284475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.911458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.553300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.136465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.795196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:29.794549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.393715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.028060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.647902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.218257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.879336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:29.879760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.483789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.129806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.746560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.301465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:33.260977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:29.985940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.578764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.265070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.842136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.396602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:33.349932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.086013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:30.686196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.369240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:31.946918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:32.492541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:07:40.219961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설구분명시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명수용가능면적연락처위도경도
연번1.0000.0001.0001.0001.0000.6990.8370.7310.6880.4880.8500.0000.607
시설구분명0.0001.0001.0001.0001.0000.2500.0000.0000.0000.6061.0000.0000.246
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동코드0.6990.2501.0001.0001.0001.0001.0000.7840.9480.0000.0000.6440.658
행정동명0.8370.0001.0001.0001.0001.0001.0001.0001.0000.0000.8430.9260.745
법정동코드0.7310.0001.0001.0001.0000.7841.0001.0001.0000.0000.6800.5590.698
법정동명0.6880.0001.0001.0001.0000.9481.0001.0001.0000.0000.9210.9530.724
수용가능면적0.4880.6061.0001.0001.0000.0000.0000.0000.0001.0001.0000.0000.120
연락처0.8501.0001.0001.0001.0000.0000.8430.6800.9211.0001.0000.9620.911
위도0.0000.0001.0001.0001.0000.6440.9260.5590.9530.0000.9621.0000.000
경도0.6070.2461.0001.0001.0000.6580.7450.6980.7240.1200.9110.0001.000
2023-12-12T15:07:40.373702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명시설구분명
법정동명1.0000.000
시설구분명0.0001.000
2023-12-12T15:07:40.488896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동코드법정동코드수용가능면적위도경도시설구분명법정동명
연번1.000-0.367-0.357-0.5340.0330.0710.0000.337
행정동코드-0.3671.0000.899-0.327-0.022-0.3860.0000.714
법정동코드-0.3570.8991.000-0.337-0.212-0.6470.0000.809
수용가능면적-0.534-0.327-0.3371.0000.0510.1940.4130.000
위도0.033-0.022-0.2120.0511.0000.6550.0000.544
경도0.071-0.386-0.6470.1940.6551.0000.1960.310
시설구분명0.0000.0000.0000.4130.0000.1961.0000.000
법정동명0.3370.7140.8090.0000.5440.3100.0001.000

Missing values

2023-12-12T15:07:33.549193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:07:33.800645image/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.
2023-12-12T15:07:33.968484image/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

연번시설구분명시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명수용가능면적수용가능면적단위연락처위도경도데이터기준일자
01학교운동장만년초등학교운동장대전광역시 서구 만년동 280 대전만년초등교대전광역시 서구 만년남로 14(만년동)3017065000만년동3017012800만년동4180.0제곱미터042-485-576536.366768127.3773812022-09-23
12학교운동장탄방중학교운동장대전광역시 서구 둔산동 1512 탄방중학교대전광역시 서구 문정로 181(둔산동)3017063000둔산1동3017011200둔산동5232.0제곱미터042-380-310136.350679127.3943832022-09-23
23학교운동장기성중학교운동장대전광역시 서구 흑석동 295 기성중학교대전광역시 서구 장안로 54(흑석동)3017060000기성동3017011700흑석동15147.0제곱미터042-610-020036.250011127.3413922022-09-23
34학교운동장선유초등학교운동장대전광역시 서구 관저동 1592 대전선유초등학교대전광역시 서구 관저남로 60(관저동)3017059700관저2동3017011600관저동2693.0제곱미터042-540-518336.294602127.3411632022-09-23
45학교운동장구봉초등학교운동장대전광역시 서구 관저동 1133 대전구봉초등학교대전광역시 서구 관저로 75(관저동)3017059700관저2동3017011600관저동4678.0제곱미터042-542-019336.299518127.3299682022-09-23
56주차장(옥외)관저동공영주차장대전광역시 서구 관저동 1510-5<NA>3017059700관저2동3017011600관저동3491.0제곱미터042-288-4125<NA><NA>2022-09-23
69학교운동장가수원초등학교운동장대전광역시 서구 가수원동 471 가수원초등학교대전광역시 서구 벌곡로 1359(가수원동)3017059000가수원동3017011400가수원동9914.0제곱미터042-541-358236.302674127.3536152022-09-23
710학교운동장가수원중학교운동장'대전광역시 서구 가수원동 806-18 가수원중학교대전광역시 서구 가수원로 26(가수원동)3017059000가수원동3017011400가수원동3687.0제곱미터042-541-014536.298097127.354642022-09-23
811학교운동장월평초등학교운동장대전광역시 서구 월평동 314 월평초등학교대전광역시 서구 월평서로 22(월평동)3017058600월평1동3017011300월평동6400.0제곱미터042-483-376536.355813127.3577992022-09-23
912학교운동장가장초등학교운동장대전광역시 서구 가장동 25-1 대전가장초등학교대전광역시 서구 가장로 150(가장동)3017057000가장동3017010900가장동4566.0제곱미터042-527-760436.330617127.3884972022-09-23
연번시설구분명시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명수용가능면적수용가능면적단위연락처위도경도데이터기준일자
2326학교운동장대전선암초등학교운동장대전광역시 서구 관저동 1152 대전선암초등학교대전광역시 서구 관저중로 61(관저동)3017059700관저2동3017011600관저동283.0제곱미터042-542-222436.297283127.3340022022-09-23
2427학교운동장대전금동초등학교운동장대전광역시 서구 관저동 1141 대전금동초등학교대전광역시 서구 관저로 45(관저동)3017059700관저2동3017011600관저동243.0제곱미터042-542-333136.298161127.3262732022-09-23
2529학교운동장대전갑천초등학교운동장대전광역시 서구 월평동 308대전광역시 서구 월평동로 61(월평동)3017058800월평3동3017011300월평동850.0제곱미터042-488-332336.360535127.3676962022-09-23
268학교운동장도안중학교운동장대전광역시 서구 도안동 1367 대전도안중학교대전광역시 서구 원도안로 160(도안동)3017059300도안동3017011500도안동2913.0제곱미터042-333-216236.31781127.3457882022-09-23
2728학교운동장대전도안초등학교운동장대전광역시 서구 도안동 1363 도안초등학교대전광역시 서구 원도안로 200(도안동)3017059300도안동3017011500도안동707.0제곱미터042-543-780336.321508127.3446352022-09-23
2830학교운동장대전삼천중학교운동장대전광역시 서구 둔산동 911 삼천중학교대전광역시 서구 문정로 271(둔산동)3017064000둔산2동3017011200둔산동779.0제곱미터042-483-673336.358332127.3943642022-09-23
2931학교운동장대전둔원중학교운동장대전광역시 서구 갈마동 1416 둔원중학교대전광역시 서구 괴정로11번길 73(갈마동)3017058200갈마2동3017011100갈마동987.0제곱미터042-530-320636.342436127.3767432022-09-23
3032학교운동장대전내동초등학교운동장대전광역시 서구 내동 13-1 대전내동초등학교대전광역시 서구 동서대로1063번길 13(내동)3017057500내동3017011000내동918.0제곱미터042-535-879136.330846127.3790212022-09-23
3133학교운동장대전신계초등학교운동장대전광역시 서구 복수동 452 신계초등학교대전광역시 서구 복수동로 26(복수동)3017051000복수동3017010100복수동592.0제곱미터042-586-586936.301706127.3780492022-09-23
327학교운동장목원대학교운동장대전광역시 서구 도안동 800 목원대학교대전광역시 서구 도안북로 88(도안동)3017059300도안동3017011500도안동7468.0제곱미터042-829-718436.3246127.3387862022-09-23