Overview

Dataset statistics

Number of variables7
Number of observations73
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory61.8 B

Variable types

Numeric4
Categorical1
Text2

Dataset

Description대전광역시 동구 재난예경보시스템 현황에 대한 데이터로 재난예경보시스템 시설구분, 세부지점명, 설치장소 등에 대한 데이터입니다.
Author대전광역시 동구
URLhttps://www.data.go.kr/data/15067221/fileData.do

Alerts

구분 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
연번 has unique valuesUnique
세부 지점명 has unique valuesUnique
설치장소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:58:04.899567
Analysis finished2023-12-12 08:58:08.212374
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T17:58:08.308286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.6
Q119
median37
Q355
95-th percentile69.4
Maximum73
Range72
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.217131
Coefficient of variation (CV)0.57343598
Kurtosis-1.2
Mean37
Median Absolute Deviation (MAD)18
Skewness0
Sum2701
Variance450.16667
MonotonicityStrictly increasing
2023-12-12T17:58:08.509393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
56 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
Other values (63) 63
86.3%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
재난방송
73 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재난방송 73
100.0%

Length

2023-12-12T17:58:08.698534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:58:08.820308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재난방송 73
100.0%

세부 지점명
Text

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-12T17:58:09.116985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length12.39726
Min length7

Characters and Unicode

Total characters905
Distinct characters126
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)100.0%

Sample

1st row가양동(가양1동 행정복지센터)
2nd row가양동(가양2동 행정복지센터)
3rd row갸양동(갱이경로당)
4th row구도동(팽나무수변공원)
5th row낭월동(낭월동 현장지원센터)
ValueCountFrequency (%)
행정복지센터 16
 
13.2%
정류장 8
 
6.6%
마을회관 7
 
5.8%
앞 3
 
2.5%
주차장 2
 
1.7%
추동(대청호 2
 
1.7%
세천동(대청동 2
 
1.7%
소제동(중앙동 2
 
1.7%
장척동(장척동 2
 
1.7%
이사동(이사동제1경로당 2
 
1.7%
Other values (75) 75
62.0%
2023-12-12T17:58:09.795025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 110
 
12.2%
( 73
 
8.1%
) 73
 
8.1%
48
 
5.3%
정 26
 
2.9%
로 20
 
2.2%
지 19
 
2.1%
당 19
 
2.1%
터 18
 
2.0%
경 18
 
2.0%
Other values (116) 481
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 696
76.9%
Open Punctuation 73
 
8.1%
Close Punctuation 73
 
8.1%
Space Separator 48
 
5.3%
Decimal Number 15
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 110
 
15.8%
정 26
 
3.7%
로 20
 
2.9%
지 19
 
2.7%
당 19
 
2.7%
터 18
 
2.6%
경 18
 
2.6%
센 17
 
2.4%
소 17
 
2.4%
복 17
 
2.4%
Other values (109) 415
59.6%
Decimal Number
ValueCountFrequency (%)
1 9
60.0%
2 4
26.7%
3 1
 
6.7%
9 1
 
6.7%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Space Separator
ValueCountFrequency (%)
48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 696
76.9%
Common 209
 
23.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 110
 
15.8%
정 26
 
3.7%
로 20
 
2.9%
지 19
 
2.7%
당 19
 
2.7%
터 18
 
2.6%
경 18
 
2.6%
센 17
 
2.4%
소 17
 
2.4%
복 17
 
2.4%
Other values (109) 415
59.6%
Common
ValueCountFrequency (%)
( 73
34.9%
) 73
34.9%
48
23.0%
1 9
 
4.3%
2 4
 
1.9%
3 1
 
0.5%
9 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 696
76.9%
ASCII 209
 
23.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 110
 
15.8%
정 26
 
3.7%
로 20
 
2.9%
지 19
 
2.7%
당 19
 
2.7%
터 18
 
2.6%
경 18
 
2.6%
센 17
 
2.4%
소 17
 
2.4%
복 17
 
2.4%
Other values (109) 415
59.6%
ASCII
ValueCountFrequency (%)
( 73
34.9%
) 73
34.9%
48
23.0%
1 9
 
4.3%
2 4
 
1.9%
3 1
 
0.5%
9 1
 
0.5%

설치장소
Text

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-12T17:58:10.484016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length24
Mean length12.671233
Min length6

Characters and Unicode

Total characters925
Distinct characters155
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

Unique73 ?
Unique (%)100.0%

Sample

1st row가양1동 행정복지센터 3층
2nd row가양2동 행정복지센터 1층
3rd row가양1동 갱이경로당 2층
4th row팽나무수변공원 내부
5th row낭월동 현장지원센터 2층
ValueCountFrequency (%)
1층 28
 
12.6%
행정복지센터 16
 
7.2%
옆 11
 
4.9%
인근 11
 
4.9%
2층 9
 
4.0%
정류장 8
 
3.6%
마을회관 7
 
3.1%
앞 5
 
2.2%
효평동 4
 
1.8%
입구 3
 
1.3%
Other values (110) 121
54.3%
2023-12-12T17:58:11.276260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
151
 
16.3%
동 47
 
5.1%
1 44
 
4.8%
층 39
 
4.2%
정 25
 
2.7%
로 22
 
2.4%
지 20
 
2.2%
2 20
 
2.2%
당 19
 
2.1%
장 19
 
2.1%
Other values (145) 519
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 662
71.6%
Space Separator 151
 
16.3%
Decimal Number 87
 
9.4%
Open Punctuation 9
 
1.0%
Close Punctuation 9
 
1.0%
Dash Punctuation 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 47
 
7.1%
층 39
 
5.9%
정 25
 
3.8%
로 22
 
3.3%
지 20
 
3.0%
당 19
 
2.9%
장 19
 
2.9%
터 18
 
2.7%
경 18
 
2.7%
복 17
 
2.6%
Other values (131) 418
63.1%
Decimal Number
ValueCountFrequency (%)
1 44
50.6%
2 20
23.0%
7 4
 
4.6%
9 4
 
4.6%
3 4
 
4.6%
4 3
 
3.4%
6 2
 
2.3%
5 2
 
2.3%
0 2
 
2.3%
8 2
 
2.3%
Space Separator
ValueCountFrequency (%)
151
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 662
71.6%
Common 263
 
28.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 47
 
7.1%
층 39
 
5.9%
정 25
 
3.8%
로 22
 
3.3%
지 20
 
3.0%
당 19
 
2.9%
장 19
 
2.9%
터 18
 
2.7%
경 18
 
2.7%
복 17
 
2.6%
Other values (131) 418
63.1%
Common
ValueCountFrequency (%)
151
57.4%
1 44
 
16.7%
2 20
 
7.6%
( 9
 
3.4%
) 9
 
3.4%
- 7
 
2.7%
7 4
 
1.5%
9 4
 
1.5%
3 4
 
1.5%
4 3
 
1.1%
Other values (4) 8
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 662
71.6%
ASCII 263
 
28.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
151
57.4%
1 44
 
16.7%
2 20
 
7.6%
( 9
 
3.4%
) 9
 
3.4%
- 7
 
2.7%
7 4
 
1.5%
9 4
 
1.5%
3 4
 
1.5%
4 3
 
1.1%
Other values (4) 8
 
3.0%
Hangul
ValueCountFrequency (%)
동 47
 
7.1%
층 39
 
5.9%
정 25
 
3.8%
로 22
 
3.3%
지 20
 
3.0%
당 19
 
2.9%
장 19
 
2.9%
터 18
 
2.7%
경 18
 
2.7%
복 17
 
2.6%
Other values (131) 418
63.1%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.326952
Minimum36.210368
Maximum36.417673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T17:58:11.983401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.210368
5-th percentile36.234161
Q136.279723
median36.334451
Q336.370371
95-th percentile36.411897
Maximum36.417673
Range0.20730541
Interquartile range (IQR)0.09064838

Descriptive statistics

Standard deviation0.054850567
Coefficient of variation (CV)0.0015099138
Kurtosis-0.79533192
Mean36.326952
Median Absolute Deviation (MAD)0.04118593
Skewness-0.27917727
Sum2651.8675
Variance0.0030085847
MonotonicityNot monotonic
2023-12-12T17:58:12.262135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.34701113 1
 
1.4%
36.37577614 1
 
1.4%
36.4050221 1
 
1.4%
36.41590553 1
 
1.4%
36.33032815 1
 
1.4%
36.41425145 1
 
1.4%
36.35799714 1
 
1.4%
36.25473266 1
 
1.4%
36.25480125 1
 
1.4%
36.3354895 1
 
1.4%
Other values (63) 63
86.3%
ValueCountFrequency (%)
36.21036795 1
1.4%
36.21940193 1
1.4%
36.22377778 1
1.4%
36.22839876 1
1.4%
36.23800177 1
1.4%
36.24288177 1
1.4%
36.24294167 1
1.4%
36.25252086 1
1.4%
36.25473266 1
1.4%
36.25480125 1
1.4%
ValueCountFrequency (%)
36.41767336 1
1.4%
36.41590553 1
1.4%
36.41534151 1
1.4%
36.41425145 1
1.4%
36.41032667 1
1.4%
36.40915533 1
1.4%
36.4050221 1
1.4%
36.40277679 1
1.4%
36.39919644 1
1.4%
36.38656865 1
1.4%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.46488
Minimum127.42217
Maximum127.53594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T17:58:12.577944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.42217
5-th percentile127.43206
Q1127.4427
median127.46401
Q3127.479
95-th percentile127.50836
Maximum127.53594
Range0.1137657
Interquartile range (IQR)0.0363082

Descriptive statistics

Standard deviation0.02552535
Coefficient of variation (CV)0.00020025399
Kurtosis0.024808212
Mean127.46488
Median Absolute Deviation (MAD)0.0197203
Skewness0.62102303
Sum9304.9362
Variance0.0006515435
MonotonicityNot monotonic
2023-12-12T17:58:12.935453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.441433 1
 
1.4%
127.4741623 1
 
1.4%
127.49177 1
 
1.4%
127.4910344 1
 
1.4%
127.4315602 1
 
1.4%
127.5359352 1
 
1.4%
127.4758747 1
 
1.4%
127.4364835 1
 
1.4%
127.4365187 1
 
1.4%
127.4487262 1
 
1.4%
Other values (63) 63
86.3%
ValueCountFrequency (%)
127.4221695 1
1.4%
127.4252848 1
1.4%
127.4257114 1
1.4%
127.4315602 1
1.4%
127.4323939 1
1.4%
127.4364835 1
1.4%
127.4365187 1
1.4%
127.4374895 1
1.4%
127.4380504 1
1.4%
127.4386264 1
1.4%
ValueCountFrequency (%)
127.5359352 1
1.4%
127.5327386 1
1.4%
127.5164723 1
1.4%
127.509775 1
1.4%
127.5074091 1
1.4%
127.5062594 1
1.4%
127.5031858 1
1.4%
127.4992048 1
1.4%
127.4933685 1
1.4%
127.492633 1
1.4%

구축년
Real number (ℝ)

Distinct10
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.0959
Minimum2012
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T17:58:13.316828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2014
Q12015
median2018
Q32018
95-th percentile2020
Maximum2022
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8941752
Coefficient of variation (CV)0.00093906054
Kurtosis0.082222531
Mean2017.0959
Median Absolute Deviation (MAD)1
Skewness-0.053325022
Sum147248
Variance3.5878995
MonotonicityNot monotonic
2023-12-12T17:58:13.601354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2018 27
37.0%
2015 16
21.9%
2017 13
17.8%
2014 4
 
5.5%
2020 4
 
5.5%
2019 3
 
4.1%
2021 2
 
2.7%
2016 2
 
2.7%
2022 1
 
1.4%
2012 1
 
1.4%
ValueCountFrequency (%)
2012 1
 
1.4%
2014 4
 
5.5%
2015 16
21.9%
2016 2
 
2.7%
2017 13
17.8%
2018 27
37.0%
2019 3
 
4.1%
2020 4
 
5.5%
2021 2
 
2.7%
2022 1
 
1.4%
ValueCountFrequency (%)
2022 1
 
1.4%
2021 2
 
2.7%
2020 4
 
5.5%
2019 3
 
4.1%
2018 27
37.0%
2017 13
17.8%
2016 2
 
2.7%
2015 16
21.9%
2014 4
 
5.5%
2012 1
 
1.4%

Interactions

2023-12-12T17:58:07.276987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:05.309982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:05.888139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:06.536473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:07.467003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:05.450367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:06.033924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:06.701235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:07.640551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:05.612082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:06.177985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:06.876908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:07.801722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:05.764167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:06.382126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:07.094824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:58:13.785551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세부 지점명설치장소위도경도구축년
연번1.0001.0001.0000.6360.5820.344
세부 지점명1.0001.0001.0001.0001.0001.000
설치장소1.0001.0001.0001.0001.0001.000
위도0.6361.0001.0001.0000.5630.578
경도0.5821.0001.0000.5631.0000.000
구축년0.3441.0001.0000.5780.0001.000
2023-12-12T17:58:13.955514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도구축년
연번1.0000.294-0.055-0.131
위도0.2941.0000.505-0.178
경도-0.0550.5051.000-0.055
구축년-0.131-0.178-0.0551.000

Missing values

2023-12-12T17:58:07.975125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:58:08.148647image/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

연번구분세부 지점명설치장소위도경도구축년
01재난방송가양동(가양1동 행정복지센터)가양1동 행정복지센터 3층36.347011127.4414332018
12재난방송가양동(가양2동 행정복지센터)가양2동 행정복지센터 1층36.349059127.4473962017
23재난방송갸양동(갱이경로당)가양1동 갱이경로당 2층36.342166127.4447142019
34재난방송구도동(팽나무수변공원)팽나무수변공원 내부36.270953127.4717752022
45재난방송낭월동(낭월동 현장지원센터)낭월동 현장지원센터 2층36.280679127.4682932019
56재난방송낭월동(산내동 행정복지센터)산내동 행정복지센터 1층36.276353127.467172018
67재난방송낭월동(초지공원)초지공원 건너편 실외 화장실 옆36.276708127.4640072014
78재난방송대동(대동 행정복지센터)대동 행정복지센터 1층36.330114127.4427722018
89재난방송대별동(대별동 정류장)대별동 정류장 옆36.279278127.4569912020
910재난방송대별동(안대별 정류장)안대별 정류장 옆36.286959127.4559022019
연번구분세부 지점명설치장소위도경도구축년
6364재난방송하소동(먹티교)먹티교 인근 (하소동 475-2 인근)36.210368127.4386992020
6465재난방송하소동(하소동제1경로당)하소동제1경로당 2층36.219402127.4415882018
6566재난방송하소동(하소동제3경로당)하소동제3경로당 1층36.223778127.4426552018
6667재난방송홍도동(홍도동 행정복지센터)홍도동 행정복지센터 서고 우측 옥상36.348962127.4257112017
6768재난방송효동(대전천)킹스타볼링장 앞 (효동 272-25 인근)36.317713127.4388172015
6869재난방송효동(효동 행정복지센터)효동 행정복지센터 1층36.317059127.4418192018
6970재난방송효평동(당산마을)당산마을 마을 입구36.402777127.4790922017
7071재난방송효평동(효들경로당 앞)효평동 효들경로당 입구 창고 앞 (효평동 209-10 인근)36.409155127.4735862017
7172재난방송효평동(효평동 마을입구)효평동 마을입구 도로 (효평동 214 인근)36.410327127.4733452015
7273재난방송효평동(효평동경로당)효평동경로당 2층36.399196127.4672712015