Overview

Dataset statistics

Number of variables10
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory89.4 B

Variable types

Numeric5
Categorical1
Text3
DateTime1

Dataset

Description대전광역시 유성구 관내에 있는 이재민 임시 주거시설 현황으로 시설명, 소재지지번주소, 소재지도로명주소, 위도, 경도, 면적, 수용인원 등의 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15113897/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
면적(제곱미터) is highly overall correlated with 수용인원(명)High correlation
수용인원(명) is highly overall correlated with 면적(제곱미터)High correlation
행정동 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
시설명 has unique valuesUnique
소재지지번주소 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:27:02.102436
Analysis finished2023-12-12 11:27:08.128362
Duration6.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T20:27:08.239787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-12T20:27:08.496466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

행정동
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
진잠동
5 
원신흥동
3 
온천2동
3 
노은1동
3 
노은3동
3 
Other values (6)
13 

Length

Max length4
Median length4
Mean length3.5333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
진잠동 5
16.7%
원신흥동 3
10.0%
온천2동 3
10.0%
노은1동 3
10.0%
노은3동 3
10.0%
노은2동 3
10.0%
구즉동 3
10.0%
신성동 2
 
6.7%
전민동 2
 
6.7%
관평동 2
 
6.7%

Length

2023-12-12T20:27:08.782858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진잠동 5
16.7%
원신흥동 3
10.0%
온천2동 3
10.0%
노은1동 3
10.0%
노은3동 3
10.0%
노은2동 3
10.0%
구즉동 3
10.0%
신성동 2
 
6.7%
전민동 2
 
6.7%
관평동 2
 
6.7%

시설명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:27:09.133860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0666667
Min length6

Characters and Unicode

Total characters182
Distinct characters52
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

Unique30 ?
Unique (%)100.0%

Sample

1st row남선초등학교
2nd row진잠초등학교
3rd row대정초등학교
4th row학하초등학교
5th row계산초등학교
ValueCountFrequency (%)
남선초등학교 1
 
3.3%
진잠초등학교 1
 
3.3%
관평초등학교 1
 
3.3%
송강초등학교 1
 
3.3%
두리초등학교 1
 
3.3%
구즉초등학교 1
 
3.3%
문지초등학교 1
 
3.3%
전민초등학교 1
 
3.3%
대덕초등학교 1
 
3.3%
금성초등학교 1
 
3.3%
Other values (20) 20
66.7%
2023-12-12T20:27:09.735495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 31
17.0%
초 30
16.5%
등 30
16.5%
교 30
16.5%
지 3
 
1.6%
송 3
 
1.6%
대 3
 
1.6%
덕 2
 
1.1%
정 2
 
1.1%
하 2
 
1.1%
Other values (42) 46
25.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 182
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 31
17.0%
초 30
16.5%
등 30
16.5%
교 30
16.5%
지 3
 
1.6%
송 3
 
1.6%
대 3
 
1.6%
덕 2
 
1.1%
정 2
 
1.1%
하 2
 
1.1%
Other values (42) 46
25.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 31
17.0%
초 30
16.5%
등 30
16.5%
교 30
16.5%
지 3
 
1.6%
송 3
 
1.6%
대 3
 
1.6%
덕 2
 
1.1%
정 2
 
1.1%
하 2
 
1.1%
Other values (42) 46
25.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 182
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 31
17.0%
초 30
16.5%
등 30
16.5%
교 30
16.5%
지 3
 
1.6%
송 3
 
1.6%
대 3
 
1.6%
덕 2
 
1.1%
정 2
 
1.1%
하 2
 
1.1%
Other values (42) 46
25.3%

소재지지번주소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:27:10.114613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length17.533333
Min length16

Characters and Unicode

Total characters526
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row대전광역시 유성구 송정동 150-1
2nd row대전광역시 유성구 원내동 62
3rd row대전광역시 유성구 대정동 310
4th row대전광역시 유성구 학하동 580-1
5th row대전광역시 유성구 계산동 254
ValueCountFrequency (%)
대전광역시 30
25.0%
유성구 30
25.0%
지족동 3
 
2.5%
하기동 2
 
1.7%
반석동 2
 
1.7%
노은동 2
 
1.7%
송강동 2
 
1.7%
원신흥동 2
 
1.7%
장대동 2
 
1.7%
396 1
 
0.8%
Other values (44) 44
36.7%
2023-12-12T20:27:10.683494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
17.1%
대 33
 
6.3%
성 31
 
5.9%
전 31
 
5.9%
동 30
 
5.7%
광 30
 
5.7%
역 30
 
5.7%
시 30
 
5.7%
유 30
 
5.7%
구 30
 
5.7%
Other values (41) 161
30.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 332
63.1%
Decimal Number 99
 
18.8%
Space Separator 90
 
17.1%
Dash Punctuation 5
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 33
9.9%
성 31
9.3%
전 31
9.3%
동 30
9.0%
광 30
9.0%
역 30
9.0%
시 30
9.0%
유 30
9.0%
구 30
9.0%
지 4
 
1.2%
Other values (29) 53
16.0%
Decimal Number
ValueCountFrequency (%)
5 16
16.2%
1 15
15.2%
6 12
12.1%
0 11
11.1%
4 11
11.1%
2 10
10.1%
9 9
9.1%
8 9
9.1%
3 4
 
4.0%
7 2
 
2.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 332
63.1%
Common 194
36.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 33
9.9%
성 31
9.3%
전 31
9.3%
동 30
9.0%
광 30
9.0%
역 30
9.0%
시 30
9.0%
유 30
9.0%
구 30
9.0%
지 4
 
1.2%
Other values (29) 53
16.0%
Common
ValueCountFrequency (%)
90
46.4%
5 16
 
8.2%
1 15
 
7.7%
6 12
 
6.2%
0 11
 
5.7%
4 11
 
5.7%
2 10
 
5.2%
9 9
 
4.6%
8 9
 
4.6%
- 5
 
2.6%
Other values (2) 6
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 332
63.1%
ASCII 194
36.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
46.4%
5 16
 
8.2%
1 15
 
7.7%
6 12
 
6.2%
0 11
 
5.7%
4 11
 
5.7%
2 10
 
5.2%
9 9
 
4.6%
8 9
 
4.6%
- 5
 
2.6%
Other values (2) 6
 
3.1%
Hangul
ValueCountFrequency (%)
대 33
9.9%
성 31
9.3%
전 31
9.3%
동 30
9.0%
광 30
9.0%
역 30
9.0%
시 30
9.0%
유 30
9.0%
구 30
9.0%
지 4
 
1.2%
Other values (29) 53
16.0%

소재지도로명주소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:27:11.029933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length19.166667
Min length16

Characters and Unicode

Total characters575
Distinct characters64
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row대전광역시 유성구 계백로93번길277-29
2nd row대전광역시 유성구 진잠로 59번길 53
3rd row대전광역시 유성구 대정로 28번길 55
4th row대전광역시 유성구 진잠옛로 165
5th row대전광역시 유성구 학하서로 74
ValueCountFrequency (%)
대전광역시 30
24.8%
유성구 30
24.8%
165 2
 
1.7%
반석동로 2
 
1.7%
62 2
 
1.7%
37 2
 
1.7%
노은서로 2
 
1.7%
28번길 1
 
0.8%
엑스포로466번길 1
 
0.8%
123-14 1
 
0.8%
Other values (48) 48
39.7%
2023-12-12T20:27:11.701008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91
15.8%
대 35
 
6.1%
전 32
 
5.6%
성 31
 
5.4%
구 31
 
5.4%
광 30
 
5.2%
역 30
 
5.2%
시 30
 
5.2%
유 30
 
5.2%
로 30
 
5.2%
Other values (54) 205
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 376
65.4%
Decimal Number 106
 
18.4%
Space Separator 91
 
15.8%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 35
9.3%
전 32
 
8.5%
성 31
 
8.2%
구 31
 
8.2%
광 30
 
8.0%
역 30
 
8.0%
시 30
 
8.0%
유 30
 
8.0%
로 30
 
8.0%
길 14
 
3.7%
Other values (42) 83
22.1%
Decimal Number
ValueCountFrequency (%)
2 18
17.0%
1 17
16.0%
5 13
12.3%
3 13
12.3%
6 13
12.3%
4 10
9.4%
7 7
 
6.6%
9 7
 
6.6%
0 5
 
4.7%
8 3
 
2.8%
Space Separator
ValueCountFrequency (%)
91
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 376
65.4%
Common 199
34.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 35
9.3%
전 32
 
8.5%
성 31
 
8.2%
구 31
 
8.2%
광 30
 
8.0%
역 30
 
8.0%
시 30
 
8.0%
유 30
 
8.0%
로 30
 
8.0%
길 14
 
3.7%
Other values (42) 83
22.1%
Common
ValueCountFrequency (%)
91
45.7%
2 18
 
9.0%
1 17
 
8.5%
5 13
 
6.5%
3 13
 
6.5%
6 13
 
6.5%
4 10
 
5.0%
7 7
 
3.5%
9 7
 
3.5%
0 5
 
2.5%
Other values (2) 5
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 376
65.4%
ASCII 199
34.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
91
45.7%
2 18
 
9.0%
1 17
 
8.5%
5 13
 
6.5%
3 13
 
6.5%
6 13
 
6.5%
4 10
 
5.0%
7 7
 
3.5%
9 7
 
3.5%
0 5
 
2.5%
Other values (2) 5
 
2.5%
Hangul
ValueCountFrequency (%)
대 35
9.3%
전 32
 
8.5%
성 31
 
8.2%
구 31
 
8.2%
광 30
 
8.0%
역 30
 
8.0%
시 30
 
8.0%
유 30
 
8.0%
로 30
 
8.0%
길 14
 
3.7%
Other values (42) 83
22.1%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.373593
Minimum36.287568
Maximum36.44264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T20:27:11.951838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.287568
5-th percentile36.306539
Q136.352735
median36.377345
Q336.392245
95-th percentile36.431908
Maximum36.44264
Range0.155072
Interquartile range (IQR)0.03951

Descriptive statistics

Standard deviation0.037767548
Coefficient of variation (CV)0.0010383233
Kurtosis0.030092321
Mean36.373593
Median Absolute Deviation (MAD)0.0174445
Skewness-0.30041057
Sum1091.2078
Variance0.0014263877
MonotonicityNot monotonic
2023-12-12T20:27:12.192777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
36.287568 1
 
3.3%
36.380594 1
 
3.3%
36.420097 1
 
3.3%
36.424096 1
 
3.3%
36.434064 1
 
3.3%
36.429272 1
 
3.3%
36.44264 1
 
3.3%
36.396264 1
 
3.3%
36.398627 1
 
3.3%
36.385055 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
36.287568 1
3.3%
36.299433 1
3.3%
36.315225 1
3.3%
36.334515 1
3.3%
36.334545 1
3.3%
36.34082 1
3.3%
36.34417 1
3.3%
36.349855 1
3.3%
36.361375 1
3.3%
36.361919 1
3.3%
ValueCountFrequency (%)
36.44264 1
3.3%
36.434064 1
3.3%
36.429272 1
3.3%
36.424096 1
3.3%
36.420097 1
3.3%
36.398627 1
3.3%
36.396264 1
3.3%
36.392902 1
3.3%
36.390274 1
3.3%
36.389117 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.33981
Minimum127.26427
Maximum127.40688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T20:27:12.423486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.26427
5-th percentile127.30084
Q1127.31372
median127.33276
Q3127.37515
95-th percentile127.39728
Maximum127.40688
Range0.142612
Interquartile range (IQR)0.06142825

Descriptive statistics

Standard deviation0.035642102
Coefficient of variation (CV)0.00027989756
Kurtosis-0.61330604
Mean127.33981
Median Absolute Deviation (MAD)0.0204595
Skewness0.31307761
Sum3820.1943
Variance0.0012703594
MonotonicityNot monotonic
2023-12-12T20:27:12.644679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
127.264271 1
 
3.3%
127.317375 1
 
3.3%
127.394821 1
 
3.3%
127.387185 1
 
3.3%
127.383672 1
 
3.3%
127.382382 1
 
3.3%
127.384719 1
 
3.3%
127.399287 1
 
3.3%
127.406883 1
 
3.3%
127.382107 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
127.264271 1
3.3%
127.299456 1
3.3%
127.302523 1
3.3%
127.303165 1
3.3%
127.310513 1
3.3%
127.311828 1
3.3%
127.312765 1
3.3%
127.313269 1
3.3%
127.315076 1
3.3%
127.317375 1
3.3%
ValueCountFrequency (%)
127.406883 1
3.3%
127.399287 1
3.3%
127.394821 1
3.3%
127.387185 1
3.3%
127.384719 1
3.3%
127.383672 1
3.3%
127.382382 1
3.3%
127.382107 1
3.3%
127.354275 1
3.3%
127.350864 1
3.3%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3125.7667
Minimum1965
Maximum5829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T20:27:12.885866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1965
5-th percentile2089.8
Q12589.25
median2772
Q33555
95-th percentile5167.05
Maximum5829
Range3864
Interquartile range (IQR)965.75

Descriptive statistics

Standard deviation954.47013
Coefficient of variation (CV)0.30535553
Kurtosis2.4429605
Mean3125.7667
Median Absolute Deviation (MAD)522.5
Skewness1.4969517
Sum93773
Variance911013.22
MonotonicityNot monotonic
2023-12-12T20:27:13.107599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2718 2
 
6.7%
2189 1
 
3.3%
2494 1
 
3.3%
2589 1
 
3.3%
2158 1
 
3.3%
3860 1
 
3.3%
3284 1
 
3.3%
2590 1
 
3.3%
2450 1
 
3.3%
5775 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
1965 1
3.3%
2034 1
3.3%
2158 1
3.3%
2174 1
3.3%
2189 1
3.3%
2450 1
3.3%
2494 1
3.3%
2589 1
3.3%
2590 1
3.3%
2594 1
3.3%
ValueCountFrequency (%)
5829 1
3.3%
5775 1
3.3%
4424 1
3.3%
3886 1
3.3%
3860 1
3.3%
3809 1
3.3%
3786 1
3.3%
3632 1
3.3%
3324 1
3.3%
3298 1
3.3%

수용인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3788.3667
Minimum2381
Maximum7065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T20:27:13.315583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2381
5-th percentile2532.5
Q13138.25
median3359.5
Q34308.75
95-th percentile6262.9
Maximum7065
Range4684
Interquartile range (IQR)1170.5

Descriptive statistics

Standard deviation1156.9855
Coefficient of variation (CV)0.30540482
Kurtosis2.4433381
Mean3788.3667
Median Absolute Deviation (MAD)633.5
Skewness1.4969991
Sum113651
Variance1338615.3
MonotonicityNot monotonic
2023-12-12T20:27:13.519130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3294 2
 
6.7%
2653 1
 
3.3%
3023 1
 
3.3%
3138 1
 
3.3%
2615 1
 
3.3%
4678 1
 
3.3%
3980 1
 
3.3%
3139 1
 
3.3%
2969 1
 
3.3%
7000 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
2381 1
3.3%
2465 1
3.3%
2615 1
3.3%
2635 1
3.3%
2653 1
3.3%
2969 1
3.3%
3023 1
3.3%
3138 1
3.3%
3139 1
3.3%
3144 1
3.3%
ValueCountFrequency (%)
7065 1
3.3%
7000 1
3.3%
5362 1
3.3%
4710 1
3.3%
4678 1
3.3%
4616 1
3.3%
4589 1
3.3%
4402 1
3.3%
4029 1
3.3%
3997 1
3.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-05-22 00:00:00
Maximum2023-05-22 00:00:00
2023-12-12T20:27:13.711668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:13.885437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T20:27:06.750149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:02.665906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:03.953244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:04.834357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:05.880274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:06.934963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:02.829672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:04.113324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:04.988480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:06.029327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:07.121531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:02.983791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:04.290228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:05.171315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:06.188496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:07.290004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:03.135655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:04.470285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:05.459251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:06.368322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:07.479553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:03.285559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:04.667708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:05.729652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:27:06.562298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:27:14.021365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동시설명소재지지번주소소재지도로명주소위도경도면적(제곱미터)수용인원(명)
연번1.0000.9181.0001.0001.0000.8850.3890.0000.000
행정동0.9181.0001.0001.0001.0000.9100.7780.0000.000
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.8850.9101.0001.0001.0001.0000.6850.0000.000
경도0.3890.7781.0001.0001.0000.6851.0000.5110.511
면적(제곱미터)0.0000.0001.0001.0001.0000.0000.5111.0001.000
수용인원(명)0.0000.0001.0001.0001.0000.0000.5111.0001.000
2023-12-12T20:27:14.238644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도면적(제곱미터)수용인원(명)행정동
연번1.0000.9790.6400.0300.0300.694
위도0.9791.0000.5810.0820.0820.689
경도0.6400.5811.0000.1450.1450.468
면적(제곱미터)0.0300.0820.1451.0001.0000.000
수용인원(명)0.0300.0820.1451.0001.0000.000
행정동0.6940.6890.4680.0000.0001.000

Missing values

2023-12-12T20:27:07.716576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:27:08.028301image/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

연번행정동시설명소재지지번주소소재지도로명주소위도경도면적(제곱미터)수용인원(명)데이터기준일자
01진잠동남선초등학교대전광역시 유성구 송정동 150-1대전광역시 유성구 계백로93번길277-2936.287568127.264271218926532023-05-22
12진잠동진잠초등학교대전광역시 유성구 원내동 62대전광역시 유성구 진잠로 59번길 5336.299433127.315076249430232023-05-22
23진잠동대정초등학교대전광역시 유성구 대정동 310대전광역시 유성구 대정로 28번길 5536.315225127.323357308537392023-05-22
34진잠동학하초등학교대전광역시 유성구 학하동 580-1대전광역시 유성구 진잠옛로 16536.334545127.311828363244022023-05-22
45진잠동계산초등학교대전광역시 유성구 계산동 254대전광역시 유성구 학하서로 7436.34417127.299456196523812023-05-22
56원신흥동원신흥초등학교대전광역시 유성구 원신흥동 496대전광역시 유성구 원신흥로55번길 3736.34082127.342731277133582023-05-22
67원신흥동흥도초등학교대전광역시 유성구 원신흥동 555대전광역시 유성구 원신흥남로 2036.334515127.338607265332152023-05-22
78원신흥동봉명초등학교대전광역시 유성구 봉명동 1023대전광역시 유성구 계룡로132번길 6236.349855127.343547293335552023-05-22
89온천1동덕송초등학교대전광역시 유성구 덕명동 498대전광역시 유성구 현충원로 26536.361375127.302523203424652023-05-22
910온천2동유성초등학교대전광역시 유성구 장대동 155대전광역시 유성구 장대로71번길 1236.363046127.333251442453622023-05-22
연번행정동시설명소재지지번주소소재지도로명주소위도경도면적(제곱미터)수용인원(명)데이터기준일자
2021노은3동외삼초등학교대전광역시 유성구 반석동 617대전광역시 유성구 반석동로 6336.392902127.310513259431442023-05-22
2122신성동금성초등학교대전광역시 유성구 신성동 210-12대전광역시 유성구 신성로72번길 536.389117127.350864577570002023-05-22
2223신성동대덕초등학교대전광역시 유성구 도룡동 396대전광역시 유성구 대덕대로556번길 10236.385055127.382107245029692023-05-22
2324전민동전민초등학교대전광역시 유성구 전민동 468대전광역시 유성구 엑스포로466번길 4236.398627127.406883271832942023-05-22
2425전민동문지초등학교대전광역시 유성구 문지동 285-9대전광역시 유성구 전민로 4136.396264127.399287259031392023-05-22
2526구즉동구즉초등학교대전광역시 유성구 봉산동 796016대전광역시 유성구 구룡달전로 3436.44264127.384719271832942023-05-22
2627구즉동두리초등학교대전광역시 유성구 송강동 162대전광역시 유성구 와룡로 3736.429272127.382382328439802023-05-22
2728구즉동송강초등학교대전광역시 유성구 송강동 196대전광역시 유성구 송강로42번길 636.434064127.383672386046782023-05-22
2829관평동관평초등학교대전광역시 유성구 관평동 900대전광역시 유성구 테크노4로 13236.424096127.387185215826152023-05-22
2930관평동용산초등학교대전광역시 유성구 용산동 662대전광역시 유성구 배울2로 10136.420097127.394821258931382023-05-22