Overview

Dataset statistics

Number of variables15
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory130.9 B

Variable types

Numeric6
Categorical4
Text5

Dataset

Description대전광역시 서구 지진실내구호소현황(순번, 시설구분명, 시설명, 지번주소, 도로명주소, 행정동코드, 행정동명, 법정동코드, 법정동명, 수용가능면적, 수용가능면적단위, 연락처, 위도, 경도, 데이터기준일자) 입니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15113167/fileData.do

Alerts

수용가능면적단위 has constant value ""Constant
데이터기준일자 has constant value ""Constant
순번 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
행정동코드 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
수용가능면적 is highly overall correlated with 시설구분명High correlation
위도 is highly overall correlated with 법정동명High correlation
경도 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
시설구분명 is highly overall correlated with 수용가능면적High correlation
법정동명 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
순번 has unique valuesUnique
시설명 has unique valuesUnique
지번주소 has unique valuesUnique
도로명주소 has unique valuesUnique
수용가능면적 has unique valuesUnique
연락처 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:17:32.477678
Analysis finished2024-04-21 02:17:39.290273
Duration6.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-21T11:17:39.360957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityNot monotonic
2024-04-21T11:17:39.771357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
23 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

시설구분명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
학교
21 
기타시설
5 
관공서
 
1

Length

Max length4
Median length2
Mean length2.4074074
Min length2

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row학교
2nd row기타시설
3rd row학교
4th row학교
5th row학교

Common Values

ValueCountFrequency (%)
학교 21
77.8%
기타시설 5
 
18.5%
관공서 1
 
3.7%

Length

2024-04-21T11:17:39.918059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:17:40.040091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
학교 21
77.8%
기타시설 5
 
18.5%
관공서 1
 
3.7%

시설명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-04-21T11:17:40.251373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.8518519
Min length5

Characters and Unicode

Total characters185
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row대전신계초등학교
2nd row오량실내테니스장
3rd row변동중학교
4th row변동초등학교
5th row서대전여자고등학교
ValueCountFrequency (%)
대전신계초등학교 1
 
3.7%
대전삼천중학교 1
 
3.7%
동방고등학교 1
 
3.7%
대전원앙초등학교 1
 
3.7%
관저중학교 1
 
3.7%
관저고등학교 1
 
3.7%
가수원중학교 1
 
3.7%
월평종합사회복지관 1
 
3.7%
통계교육원 1
 
3.7%
성룡초등학교 1
 
3.7%
Other values (17) 17
63.0%
2024-04-21T11:17:40.708583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 22
 
11.9%
학 21
 
11.4%
등 16
 
8.6%
초 12
 
6.5%
대 9
 
4.9%
전 9
 
4.9%
중 5
 
2.7%
원 5
 
2.7%
고 4
 
2.2%
동 4
 
2.2%
Other values (57) 78
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 185
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 22
 
11.9%
학 21
 
11.4%
등 16
 
8.6%
초 12
 
6.5%
대 9
 
4.9%
전 9
 
4.9%
중 5
 
2.7%
원 5
 
2.7%
고 4
 
2.2%
동 4
 
2.2%
Other values (57) 78
42.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 185
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 22
 
11.9%
학 21
 
11.4%
등 16
 
8.6%
초 12
 
6.5%
대 9
 
4.9%
전 9
 
4.9%
중 5
 
2.7%
원 5
 
2.7%
고 4
 
2.2%
동 4
 
2.2%
Other values (57) 78
42.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 185
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 22
 
11.9%
학 21
 
11.4%
등 16
 
8.6%
초 12
 
6.5%
대 9
 
4.9%
전 9
 
4.9%
중 5
 
2.7%
원 5
 
2.7%
고 4
 
2.2%
동 4
 
2.2%
Other values (57) 78
42.2%

지번주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-04-21T11:17:40.918918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length16.666667
Min length15

Characters and Unicode

Total characters450
Distinct characters41
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

Unique27 ?
Unique (%)100.0%

Sample

1st row대전광역시 서구 복수동 452
2nd row대전광역시 서구 복수동 405
3rd row대전광역시 서구 변동 54-1
4th row대전광역시 서구 변동 36-3
5th row대전광역시 서구 도마동 420-3
ValueCountFrequency (%)
대전광역시 27
25.0%
서구 27
25.0%
도마동 5
 
4.6%
월평동 4
 
3.7%
관저동 4
 
3.7%
둔산동 3
 
2.8%
변동 2
 
1.9%
복수동 2
 
1.9%
49 1
 
0.9%
304 1
 
0.9%
Other values (32) 32
29.6%
2024-04-21T11:17:41.343169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
18.0%
대 27
 
6.0%
전 27
 
6.0%
구 27
 
6.0%
서 27
 
6.0%
동 27
 
6.0%
시 27
 
6.0%
역 27
 
6.0%
광 27
 
6.0%
1 19
 
4.2%
Other values (31) 134
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 268
59.6%
Decimal Number 90
 
20.0%
Space Separator 81
 
18.0%
Dash Punctuation 11
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 27
10.1%
전 27
10.1%
구 27
10.1%
서 27
10.1%
동 27
10.1%
시 27
10.1%
역 27
10.1%
광 27
10.1%
마 6
 
2.2%
도 6
 
2.2%
Other values (19) 40
14.9%
Decimal Number
ValueCountFrequency (%)
1 19
21.1%
3 12
13.3%
4 10
11.1%
8 9
10.0%
2 8
8.9%
6 8
8.9%
5 7
 
7.8%
0 7
 
7.8%
9 6
 
6.7%
7 4
 
4.4%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 268
59.6%
Common 182
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 27
10.1%
전 27
10.1%
구 27
10.1%
서 27
10.1%
동 27
10.1%
시 27
10.1%
역 27
10.1%
광 27
10.1%
마 6
 
2.2%
도 6
 
2.2%
Other values (19) 40
14.9%
Common
ValueCountFrequency (%)
81
44.5%
1 19
 
10.4%
3 12
 
6.6%
- 11
 
6.0%
4 10
 
5.5%
8 9
 
4.9%
2 8
 
4.4%
6 8
 
4.4%
5 7
 
3.8%
0 7
 
3.8%
Other values (2) 10
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 268
59.6%
ASCII 182
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
44.5%
1 19
 
10.4%
3 12
 
6.6%
- 11
 
6.0%
4 10
 
5.5%
8 9
 
4.9%
2 8
 
4.4%
6 8
 
4.4%
5 7
 
3.8%
0 7
 
3.8%
Other values (2) 10
 
5.5%
Hangul
ValueCountFrequency (%)
대 27
10.1%
전 27
10.1%
구 27
10.1%
서 27
10.1%
동 27
10.1%
시 27
10.1%
역 27
10.1%
광 27
10.1%
마 6
 
2.2%
도 6
 
2.2%
Other values (19) 40
14.9%

도로명주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-04-21T11:17:41.555074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.481481
Min length16

Characters and Unicode

Total characters499
Distinct characters54
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

Unique27 ?
Unique (%)100.0%

Sample

1st row대전광역시 서구 복수동로 26
2nd row대전광역시 서구 유등로 135
3rd row대전광역시 서구 도산로181번길 39
4th row대전광역시 서구 유등로 373
5th row대전광역시 서구 배재로197번길 20
ValueCountFrequency (%)
대전광역시 27
25.0%
서구 27
25.0%
유등로 3
 
2.8%
26 2
 
1.9%
배재로197번길 2
 
1.9%
관저북로 2
 
1.9%
문정로 2
 
1.9%
713 1
 
0.9%
둔산북로 1
 
0.9%
232-33 1
 
0.9%
Other values (40) 40
37.0%
2024-04-21T11:17:41.989287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
106
21.2%
대 29
 
5.8%
역 28
 
5.6%
서 28
 
5.6%
광 27
 
5.4%
시 27
 
5.4%
구 27
 
5.4%
전 27
 
5.4%
로 26
 
5.2%
1 21
 
4.2%
Other values (44) 153
30.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
59.9%
Space Separator 106
 
21.2%
Decimal Number 93
 
18.6%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 29
9.7%
역 28
9.4%
서 28
9.4%
광 27
9.0%
시 27
9.0%
구 27
9.0%
전 27
9.0%
로 26
8.7%
길 9
 
3.0%
번 8
 
2.7%
Other values (32) 63
21.1%
Decimal Number
ValueCountFrequency (%)
1 21
22.6%
3 15
16.1%
6 10
10.8%
7 10
10.8%
2 9
9.7%
5 8
 
8.6%
0 7
 
7.5%
9 6
 
6.5%
4 4
 
4.3%
8 3
 
3.2%
Space Separator
ValueCountFrequency (%)
106
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
59.9%
Common 200
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 29
9.7%
역 28
9.4%
서 28
9.4%
광 27
9.0%
시 27
9.0%
구 27
9.0%
전 27
9.0%
로 26
8.7%
길 9
 
3.0%
번 8
 
2.7%
Other values (32) 63
21.1%
Common
ValueCountFrequency (%)
106
53.0%
1 21
 
10.5%
3 15
 
7.5%
6 10
 
5.0%
7 10
 
5.0%
2 9
 
4.5%
5 8
 
4.0%
0 7
 
3.5%
9 6
 
3.0%
4 4
 
2.0%
Other values (2) 4
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
59.9%
ASCII 200
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
106
53.0%
1 21
 
10.5%
3 15
 
7.5%
6 10
 
5.0%
7 10
 
5.0%
2 9
 
4.5%
5 8
 
4.0%
0 7
 
3.5%
9 6
 
3.0%
4 4
 
2.0%
Other values (2) 4
 
2.0%
Hangul
ValueCountFrequency (%)
대 29
9.7%
역 28
9.4%
서 28
9.4%
광 27
9.0%
시 27
9.0%
구 27
9.0%
전 27
9.0%
로 26
8.7%
길 9
 
3.0%
번 8
 
2.7%
Other values (32) 63
21.1%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170573 × 109
Minimum3.017051 × 109
Maximum3.017066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-21T11:17:42.135715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017051 × 109
5-th percentile3.0170513 × 109
Q13.0170535 × 109
median3.0170582 × 109
Q33.0170594 × 109
95-th percentile3.0170654 × 109
Maximum3.017066 × 109
Range15000
Interquartile range (IQR)5950

Descriptive statistics

Standard deviation4124.249
Coefficient of variation (CV)1.3669774 × 10-6
Kurtosis-0.14713343
Mean3.0170573 × 109
Median Absolute Deviation (MAD)2200
Skewness0.41514525
Sum8.1460546 × 1010
Variance17009430
MonotonicityNot monotonic
2024-04-21T11:17:42.260727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3017053000 4
14.8%
3017059600 3
11.1%
3017051000 2
 
7.4%
3017066000 2
 
7.4%
3017058700 2
 
7.4%
3017058800 2
 
7.4%
3017054000 2
 
7.4%
3017059300 1
 
3.7%
3017059700 1
 
3.7%
3017059000 1
 
3.7%
Other values (7) 7
25.9%
ValueCountFrequency (%)
3017051000 2
7.4%
3017052000 1
 
3.7%
3017053000 4
14.8%
3017054000 2
7.4%
3017055500 1
 
3.7%
3017056000 1
 
3.7%
3017057000 1
 
3.7%
3017057500 1
 
3.7%
3017058200 1
 
3.7%
3017058700 2
7.4%
ValueCountFrequency (%)
3017066000 2
7.4%
3017064000 1
 
3.7%
3017059700 1
 
3.7%
3017059600 3
11.1%
3017059300 1
 
3.7%
3017059000 1
 
3.7%
3017058800 2
7.4%
3017058700 2
7.4%
3017058200 1
 
3.7%
3017057500 1
 
3.7%

행정동명
Text

Distinct17
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-04-21T11:17:42.435893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.5555556
Min length2

Characters and Unicode

Total characters96
Distinct characters25
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

Unique10 ?
Unique (%)37.0%

Sample

1st row복수동
2nd row복수동
3rd row변동
4th row변동
5th row도마2동
ValueCountFrequency (%)
도마2동 4
14.8%
관저1동 3
11.1%
복수동 2
 
7.4%
둔산3동 2
 
7.4%
월평2동 2
 
7.4%
월평3동 2
 
7.4%
변동 2
 
7.4%
내동 1
 
3.7%
관저2동 1
 
3.7%
갈마2동 1
 
3.7%
Other values (7) 7
25.9%
2024-04-21T11:17:42.763634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
28.1%
2 9
 
9.4%
도 6
 
6.2%
마 6
 
6.2%
3 4
 
4.2%
평 4
 
4.2%
월 4
 
4.2%
1 4
 
4.2%
저 4
 
4.2%
관 4
 
4.2%
Other values (15) 24
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
82.3%
Decimal Number 17
 
17.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
34.2%
도 6
 
7.6%
마 6
 
7.6%
평 4
 
5.1%
월 4
 
5.1%
저 4
 
5.1%
관 4
 
5.1%
둔 3
 
3.8%
산 3
 
3.8%
수 3
 
3.8%
Other values (12) 15
19.0%
Decimal Number
ValueCountFrequency (%)
2 9
52.9%
3 4
23.5%
1 4
23.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
82.3%
Common 17
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
34.2%
도 6
 
7.6%
마 6
 
7.6%
평 4
 
5.1%
월 4
 
5.1%
저 4
 
5.1%
관 4
 
5.1%
둔 3
 
3.8%
산 3
 
3.8%
수 3
 
3.8%
Other values (12) 15
19.0%
Common
ValueCountFrequency (%)
2 9
52.9%
3 4
23.5%
1 4
23.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
82.3%
ASCII 17
 
17.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
34.2%
도 6
 
7.6%
마 6
 
7.6%
평 4
 
5.1%
월 4
 
5.1%
저 4
 
5.1%
관 4
 
5.1%
둔 3
 
3.8%
산 3
 
3.8%
수 3
 
3.8%
Other values (12) 15
19.0%
ASCII
ValueCountFrequency (%)
2 9
52.9%
3 4
23.5%
1 4
23.5%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170109 × 109
Minimum3.0170101 × 109
Maximum3.0170116 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-21T11:17:42.883825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0170101 × 109
5-th percentile3.0170101 × 109
Q13.0170103 × 109
median3.0170111 × 109
Q33.0170113 × 109
95-th percentile3.0170116 × 109
Maximum3.0170116 × 109
Range1500
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation543.72882
Coefficient of variation (CV)1.8022103 × 10-7
Kurtosis-1.5636389
Mean3.0170109 × 109
Median Absolute Deviation (MAD)500
Skewness-0.26234955
Sum8.1459295 × 1010
Variance295641.03
MonotonicityNot monotonic
2024-04-21T11:17:43.005485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3017010300 5
18.5%
3017011300 4
14.8%
3017011600 4
14.8%
3017011200 3
11.1%
3017010100 2
 
7.4%
3017010200 2
 
7.4%
3017010600 1
 
3.7%
3017010800 1
 
3.7%
3017010900 1
 
3.7%
3017011000 1
 
3.7%
Other values (3) 3
11.1%
ValueCountFrequency (%)
3017010100 2
 
7.4%
3017010200 2
 
7.4%
3017010300 5
18.5%
3017010600 1
 
3.7%
3017010800 1
 
3.7%
3017010900 1
 
3.7%
3017011000 1
 
3.7%
3017011100 1
 
3.7%
3017011200 3
11.1%
3017011300 4
14.8%
ValueCountFrequency (%)
3017011600 4
14.8%
3017011500 1
 
3.7%
3017011400 1
 
3.7%
3017011300 4
14.8%
3017011200 3
11.1%
3017011100 1
 
3.7%
3017011000 1
 
3.7%
3017010900 1
 
3.7%
3017010800 1
 
3.7%
3017010600 1
 
3.7%

법정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
도마동
5 
월평동
4 
관저동
4 
둔산동
3 
복수동
2 
Other values (8)
9 

Length

Max length4
Median length3
Mean length2.9259259
Min length2

Unique

Unique7 ?
Unique (%)25.9%

Sample

1st row복수동
2nd row복수동
3rd row변동
4th row변동
5th row도마동

Common Values

ValueCountFrequency (%)
도마동 5
18.5%
월평동 4
14.8%
관저동 4
14.8%
둔산동 3
11.1%
복수동 2
 
7.4%
변동 2
 
7.4%
탄방동 1
 
3.7%
괴정동 1
 
3.7%
가장동 1
 
3.7%
내동 1
 
3.7%
Other values (3) 3
11.1%

Length

2024-04-21T11:17:43.161502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도마동 5
18.5%
월평동 4
14.8%
관저동 4
14.8%
둔산동 3
11.1%
복수동 2
 
7.4%
변동 2
 
7.4%
탄방동 1
 
3.7%
괴정동 1
 
3.7%
가장동 1
 
3.7%
내동 1
 
3.7%
Other values (3) 3
11.1%

수용가능면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1350.7778
Minimum72
Maximum6216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-21T11:17:43.325397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile226.7
Q1685
median918
Q31769
95-th percentile2917.2
Maximum6216
Range6144
Interquartile range (IQR)1084

Descriptive statistics

Standard deviation1222.7121
Coefficient of variation (CV)0.90519113
Kurtosis9.2084973
Mean1350.7778
Median Absolute Deviation (MAD)313
Skewness2.6433126
Sum36471
Variance1495024.8
MonotonicityNot monotonic
2024-04-21T11:17:43.522662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
605 1
 
3.7%
2458 1
 
3.7%
707 1
 
3.7%
1458 1
 
3.7%
665 1
 
3.7%
648 1
 
3.7%
3114 1
 
3.7%
899 1
 
3.7%
72 1
 
3.7%
6216 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
72 1
3.7%
206 1
3.7%
275 1
3.7%
605 1
3.7%
623 1
3.7%
648 1
3.7%
665 1
3.7%
705 1
3.7%
707 1
3.7%
842 1
3.7%
ValueCountFrequency (%)
6216 1
3.7%
3114 1
3.7%
2458 1
3.7%
2423 1
3.7%
1999 1
3.7%
1919 1
3.7%
1871 1
3.7%
1667 1
3.7%
1552 1
3.7%
1458 1
3.7%

수용가능면적단위
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
㎡
27 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row㎡
2nd row㎡
3rd row㎡
4th row㎡
5th row㎡

Common Values

ValueCountFrequency (%)
㎡ 27
100.0%

Length

2024-04-21T11:17:43.654407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:17:43.749645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
㎡ 27
100.0%

연락처
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-04-21T11:17:43.911377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters324
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

Unique27 ?
Unique (%)100.0%

Sample

1st row042-586-5380
2nd row042-586-0755
3rd row042-530-8503
4th row042-527-7915
5th row042-520-6105
ValueCountFrequency (%)
042-586-5380 1
 
3.7%
042-483-6733 1
 
3.7%
042-333-5803 1
 
3.7%
042-544-8673 1
 
3.7%
042-540-5881 1
 
3.7%
042-540-2800 1
 
3.7%
042-541-0145 1
 
3.7%
042-484-6181 1
 
3.7%
042-366-6433 1
 
3.7%
042-483-6865 1
 
3.7%
Other values (17) 17
63.0%
2024-04-21T11:17:44.239719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 54
16.7%
0 50
15.4%
4 46
14.2%
2 42
13.0%
5 36
11.1%
3 26
8.0%
8 24
7.4%
6 18
 
5.6%
7 13
 
4.0%
1 12
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
83.3%
Dash Punctuation 54
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50
18.5%
4 46
17.0%
2 42
15.6%
5 36
13.3%
3 26
9.6%
8 24
8.9%
6 18
 
6.7%
7 13
 
4.8%
1 12
 
4.4%
9 3
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 324
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 54
16.7%
0 50
15.4%
4 46
14.2%
2 42
13.0%
5 36
11.1%
3 26
8.0%
8 24
7.4%
6 18
 
5.6%
7 13
 
4.0%
1 12
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 54
16.7%
0 50
15.4%
4 46
14.2%
2 42
13.0%
5 36
11.1%
3 26
8.0%
8 24
7.4%
6 18
 
5.6%
7 13
 
4.0%
1 12
 
3.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.329365
Minimum36.298097
Maximum36.363825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-21T11:17:44.375673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.298097
5-th percentile36.300831
Q136.31219
median36.324894
Q336.347632
95-th percentile36.361284
Maximum36.363825
Range0.06572791
Interquartile range (IQR)0.03544278

Descriptive statistics

Standard deviation0.021280479
Coefficient of variation (CV)0.00058576524
Kurtosis-1.2821346
Mean36.329365
Median Absolute Deviation (MAD)0.01754173
Skewness0.24385149
Sum980.89285
Variance0.00045285879
MonotonicityNot monotonic
2024-04-21T11:17:44.499559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
36.3017064 1
 
3.7%
36.30493684 1
 
3.7%
36.32150849 1
 
3.7%
36.30045603 1
 
3.7%
36.31114142 1
 
3.7%
36.30537208 1
 
3.7%
36.31058792 1
 
3.7%
36.2980968 1
 
3.7%
36.36382471 1
 
3.7%
36.35854613 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
36.2980968 1
3.7%
36.30045603 1
3.7%
36.3017064 1
3.7%
36.30493684 1
3.7%
36.30537208 1
3.7%
36.31058792 1
3.7%
36.31114142 1
3.7%
36.31323799 1
3.7%
36.3135717 1
3.7%
36.31471214 1
3.7%
ValueCountFrequency (%)
36.36382471 1
3.7%
36.36160443 1
3.7%
36.36053489 1
3.7%
36.35854613 1
3.7%
36.35833209 1
3.7%
36.35397834 1
3.7%
36.35064995 1
3.7%
36.34461502 1
3.7%
36.34243608 1
3.7%
36.34056272 1
3.7%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.37205
Minimum127.33688
Maximum127.401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-21T11:17:44.618408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.33688
5-th percentile127.33851
Q1127.36792
median127.37589
Q3127.38504
95-th percentile127.39551
Maximum127.401
Range0.0641225
Interquartile range (IQR)0.0171259

Descriptive statistics

Standard deviation0.017894022
Coefficient of variation (CV)0.00014048625
Kurtosis-0.29089452
Mean127.37205
Median Absolute Deviation (MAD)0.0089928
Skewness-0.69056641
Sum3439.0455
Variance0.00032019603
MonotonicityNot monotonic
2024-04-21T11:17:44.769496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
127.3780492 1
 
3.7%
127.379764 1
 
3.7%
127.344635 1
 
3.7%
127.3464226 1
 
3.7%
127.3368772 1
 
3.7%
127.339861 1
 
3.7%
127.3379294 1
 
3.7%
127.3546399 1
 
3.7%
127.3690148 1
 
3.7%
127.3786501 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
127.3368772 1
3.7%
127.3379294 1
3.7%
127.339861 1
3.7%
127.344635 1
3.7%
127.3464226 1
3.7%
127.3546399 1
3.7%
127.3676964 1
3.7%
127.3681349 1
3.7%
127.3688328 1
3.7%
127.3690148 1
3.7%
ValueCountFrequency (%)
127.4009997 1
3.7%
127.3959985 1
3.7%
127.3943644 1
3.7%
127.3884971 1
3.7%
127.3855644 1
3.7%
127.3854877 1
3.7%
127.3851971 1
3.7%
127.384886 1
3.7%
127.379764 1
3.7%
127.3790208 1
3.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-04-01
27 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-01
2nd row2024-04-01
3rd row2024-04-01
4th row2024-04-01
5th row2024-04-01

Common Values

ValueCountFrequency (%)
2024-04-01 27
100.0%

Length

2024-04-21T11:17:44.925870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:17:45.032886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-01 27
100.0%

Interactions

2024-04-21T11:17:38.238257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.009342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.691734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.393030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.074377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.645716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:38.387283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.151161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.805951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.476934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.161988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.728134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:38.504659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.255167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.919467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.593490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.253994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.815578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:38.621130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.345939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.047840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.722352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.361637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.911215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:38.725107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.498490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.149840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.844765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.463385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:38.006549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:38.829953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:35.595356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.250537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:36.957780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:37.548014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:38.121035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:17:45.156046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시설구분명시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명수용가능면적연락처위도경도
순번1.0000.6131.0001.0001.0000.6140.9070.8240.8490.4991.0000.6590.467
시설구분명0.6131.0001.0001.0001.0000.0000.0000.0000.0000.9131.0000.0000.000
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
행정동코드0.6140.0001.0001.0001.0001.0001.0000.8130.9240.0001.0000.6480.664
행정동명0.9070.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.8960.937
법정동코드0.8240.0001.0001.0001.0000.8131.0001.0001.0000.0001.0000.9210.800
법정동명0.8490.0001.0001.0001.0000.9241.0001.0001.0000.0001.0000.8570.820
수용가능면적0.4990.9131.0001.0001.0000.0000.0000.0000.0001.0001.0000.0000.000
연락처1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.6590.0001.0001.0001.0000.6480.8960.9210.8570.0001.0001.0000.712
경도0.4670.0001.0001.0001.0000.6640.9370.8000.8200.0001.0000.7121.000
2024-04-21T11:17:45.336647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분명법정동명
시설구분명1.0000.000
법정동명0.0001.000
2024-04-21T11:17:45.432103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번행정동코드법정동코드수용가능면적위도경도시설구분명법정동명
순번1.0000.8470.993-0.1410.059-0.5040.3710.480
행정동코드0.8471.0000.854-0.1230.231-0.1450.0000.681
법정동코드0.9930.8541.000-0.1310.065-0.5210.0000.882
수용가능면적-0.141-0.123-0.1311.000-0.107-0.0490.6010.000
위도0.0590.2310.065-0.1071.0000.4270.0000.513
경도-0.504-0.145-0.521-0.0490.4271.0000.0000.455
시설구분명0.3710.0000.0000.6010.0000.0001.0000.000
법정동명0.4800.6810.8820.0000.5130.4550.0001.000

Missing values

2024-04-21T11:17:38.998743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:17:39.199939image/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

순번시설구분명시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명수용가능면적수용가능면적단위연락처위도경도데이터기준일자
01학교대전신계초등학교대전광역시 서구 복수동 452대전광역시 서구 복수동로 263017051000복수동3017010100복수동605㎡042-586-538036.301706127.3780492024-04-01
12기타시설오량실내테니스장대전광역시 서구 복수동 405대전광역시 서구 유등로 1353017051000복수동3017010100복수동2458㎡042-586-075536.304937127.3797642024-04-01
23학교변동중학교대전광역시 서구 변동 54-1대전광역시 서구 도산로181번길 393017054000변동3017010200변동1919㎡042-530-850336.324272127.3758932024-04-01
34학교변동초등학교대전광역시 서구 변동 36-3대전광역시 서구 유등로 3733017054000변동3017010200변동972㎡042-527-791536.324781127.3854882024-04-01
45학교서대전여자고등학교대전광역시 서구 도마동 420-3대전광역시 서구 배재로197번길 203017053000도마2동3017010300도마동2423㎡042-520-610536.327025127.3688332024-04-01
56학교유천초등학교대전광역시 서구 도마동 149-1대전광역시 서구 제비네6길 693017053000도마2동3017010300도마동1552㎡042-522-460536.314712127.3757712024-04-01
67학교제일고등학교대전광역시 서구 도마동 155-20대전광역시 서구 배재로46번길 773017053000도마2동3017010300도마동892㎡042-527-966136.313572127.3729482024-04-01
78기타시설도마실국민체육센터대전광역시 서구 도마동 567대전광역시 서구 유등로 2353017052000도마1동3017010300도마동981㎡042-582-555636.313238127.3851972024-04-01
89기타시설도솔다목적체육관대전광역시 서구 도마동 426대전광역시 서구 배재로197번길 413017053000도마2동3017010300도마동1667㎡042-522-382036.324894127.3681352024-04-01
910기타시설서구문화원대전광역시 서구 탄방동 667대전광역시 서구 계룡로553번길 383017055500탄방동3017010600탄방동623㎡042-488-547436.344615127.3855642024-04-01
순번시설구분명시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명수용가능면적수용가능면적단위연락처위도경도데이터기준일자
1718학교대전갑천초등학교대전광역시 서구 월평동 308대전광역시 서구 월평동로 613017058800월평3동3017011300월평동850㎡042-488-332336.360535127.3676962024-04-01
1819학교성룡초등학교대전광역시 서구 월평동 304대전광역시 서구 갈마역로 1253017058800월평3동3017011300월평동1871㎡042-483-686536.361604127.3735852024-04-01
1920관공서통계교육원대전광역시 서구 월평동 282-1대전광역시 서구 한밭대로 7133017058700월평2동3017011300월평동6216㎡042-366-643336.358546127.378652024-04-01
2021기타시설월평종합사회복지관대전광역시 서구 월평동 218대전광역시 서구 월평북로 113017058700월평2동3017011300월평동72㎡042-484-618136.363825127.3690152024-04-01
2122학교가수원중학교대전광역시 서구 가수원동 806-18대전광역시 서구 가수원로 263017059000가수원동3017011400가수원동899㎡042-541-014536.298097127.354642024-04-01
2224학교관저고등학교대전광역시 서구 관저동 983대전광역시 서구 관저북로 943017059600관저1동3017011600관저동3114㎡042-540-280036.310588127.3379292024-04-01
2325학교관저중학교대전광역시 서구 관저동 993대전광역시 서구 관저동로 1433017059600관저1동3017011600관저동648㎡042-540-588136.305372127.3398612024-04-01
2426학교대전원앙초등학교대전광역시 서구 관저동 49대전광역시 서구 관저북로 973017059600관저1동3017011600관저동665㎡042-544-867336.311141127.3368772024-04-01
2527학교동방고등학교대전광역시 서구 관저동 7-1대전광역시 서구 계백로 11063017059700관저2동3017011600관저동1458㎡042-333-580336.300456127.3464232024-04-01
2623학교대전도안초등학교대전광역시 서구 도안동 1363대전광역시 서구 원도안로 2003017059300도안동3017011500도안동707㎡042-543-780336.321508127.3446352024-04-01