Overview

Dataset statistics

Number of variables10
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory86.0 B

Variable types

Numeric4
Categorical3
Text3

Dataset

Description대전광역시_재난예경보체계 구축 현황(시스템명, 설치 주소, 장소명, 설치년월, 용도 등)
Author대전광역시
URLhttps://www.data.go.kr/data/15081749/fileData.do

Alerts

용 도 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
순번 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
구축년월 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
순번 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:00:20.279233
Analysis finished2023-12-12 13:00:22.824456
Duration2.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T22:00:22.905102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q117.5
median34
Q350.5
95-th percentile63.7
Maximum67
Range66
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.485037
Coefficient of variation (CV)0.57308932
Kurtosis-1.2
Mean34
Median Absolute Deviation (MAD)17
Skewness0
Sum2278
Variance379.66667
MonotonicityStrictly increasing
2023-12-12T22:00:23.063871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
44 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
45 1
 
1.5%
43 1
 
1.5%
2 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
기상관측장비
36 
재해문자전광판
25 
재난방송 중계설비
6 

Length

Max length9
Median length6
Mean length6.641791
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기상관측장비 36
53.7%
재해문자전광판 25
37.3%
재난방송 중계설비 6
 
9.0%

Length

2023-12-12T22:00:23.576199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:00:23.697571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기상관측장비 36
49.3%
재해문자전광판 25
34.2%
재난방송 6
 
8.2%
중계설비 6
 
8.2%

용 도
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size668.0 B
하천, 일반
19 
강우량계
12 
수위측정계
8 
지진계측기
8 
라디오, DMB
6 
Other values (4)
14 

Length

Max length8
Median length7
Mean length5.1343284
Min length2

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row라디오, DMB
2nd row라디오, DMB
3rd row라디오, DMB
4th row라디오, DMB
5th row라디오, DMB

Common Values

ValueCountFrequency (%)
하천, 일반 19
28.4%
강우량계 12
17.9%
수위측정계 8
11.9%
지진계측기 8
11.9%
라디오, DMB 6
 
9.0%
일반 6
 
9.0%
적설결빙계 5
 
7.5%
자동기상측정기 2
 
3.0%
시정계 1
 
1.5%

Length

2023-12-12T22:00:23.831859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:00:23.990651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 25
27.2%
하천 19
20.7%
강우량계 12
13.0%
수위측정계 8
 
8.7%
지진계측기 8
 
8.7%
라디오 6
 
6.5%
dmb 6
 
6.5%
적설결빙계 5
 
5.4%
자동기상측정기 2
 
2.2%
시정계 1
 
1.1%

관리번호
Text

Distinct58
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-12T22:00:24.274734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.41791
Min length11

Characters and Unicode

Total characters765
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)76.1%

Sample

1st row동구-05-18001
2nd row서구-05-18001
3rd row서구-05-18002
4th row서구-05-18003
5th row유성구-05-18001
ValueCountFrequency (%)
유성구-04-20002 4
 
6.0%
중구-03-19001 2
 
3.0%
유성구-04-15001 2
 
3.0%
중구-04-20001 2
 
3.0%
동구-04-15001 2
 
3.0%
서구-04-14001 2
 
3.0%
대덕구-02-15005 2
 
3.0%
동구-04-10002 1
 
1.5%
대덕구-04-09001 1
 
1.5%
대덕구-04-10001 1
 
1.5%
Other values (48) 48
71.6%
2023-12-12T22:00:24.678812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 221
28.9%
- 134
17.5%
1 92
12.0%
구 67
 
8.8%
4 47
 
6.1%
2 31
 
4.1%
5 31
 
4.1%
3 28
 
3.7%
성 19
 
2.5%
유 19
 
2.5%
Other values (9) 76
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 469
61.3%
Other Letter 162
 
21.2%
Dash Punctuation 134
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 221
47.1%
1 92
19.6%
4 47
 
10.0%
2 31
 
6.6%
5 31
 
6.6%
3 28
 
6.0%
7 9
 
1.9%
8 6
 
1.3%
9 3
 
0.6%
6 1
 
0.2%
Other Letter
ValueCountFrequency (%)
구 67
41.4%
성 19
 
11.7%
유 19
 
11.7%
서 16
 
9.9%
동 13
 
8.0%
중 10
 
6.2%
덕 9
 
5.6%
대 9
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 603
78.8%
Hangul 162
 
21.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 221
36.7%
- 134
22.2%
1 92
15.3%
4 47
 
7.8%
2 31
 
5.1%
5 31
 
5.1%
3 28
 
4.6%
7 9
 
1.5%
8 6
 
1.0%
9 3
 
0.5%
Hangul
ValueCountFrequency (%)
구 67
41.4%
성 19
 
11.7%
유 19
 
11.7%
서 16
 
9.9%
동 13
 
8.0%
중 10
 
6.2%
덕 9
 
5.6%
대 9
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
78.8%
Hangul 162
 
21.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 221
36.7%
- 134
22.2%
1 92
15.3%
4 47
 
7.8%
2 31
 
5.1%
5 31
 
5.1%
3 28
 
4.6%
7 9
 
1.5%
8 6
 
1.0%
9 3
 
0.5%
Hangul
ValueCountFrequency (%)
구 67
41.4%
성 19
 
11.7%
유 19
 
11.7%
서 16
 
9.9%
동 13
 
8.0%
중 10
 
6.2%
덕 9
 
5.6%
대 9
 
5.6%

세부 지점명
Text

Distinct65
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-12T22:00:25.020275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length7.8656716
Min length3

Characters and Unicode

Total characters527
Distinct characters139
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)94.0%

Sample

1st row대전역지하차도
2nd row삼천지하차도
3rd row갈마지하차도
4th row도솔터널
5th row오봉터널
ValueCountFrequency (%)
옥상 10
 
8.2%
대전 6
 
4.9%
2층 6
 
4.9%
도솔터널 4
 
3.3%
입구 3
 
2.5%
대덕구청 2
 
1.6%
방향 2
 
1.6%
노은터널 2
 
1.6%
다리 2
 
1.6%
주민센터 2
 
1.6%
Other values (80) 83
68.0%
2023-12-12T22:00:25.473512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
 
11.0%
동 22
 
4.2%
교 20
 
3.8%
상 19
 
3.6%
터 17
 
3.2%
대 16
 
3.0%
도 14
 
2.7%
구 11
 
2.1%
지 11
 
2.1%
층 11
 
2.1%
Other values (129) 328
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 435
82.5%
Space Separator 58
 
11.0%
Decimal Number 16
 
3.0%
Open Punctuation 6
 
1.1%
Close Punctuation 6
 
1.1%
Dash Punctuation 3
 
0.6%
Other Punctuation 2
 
0.4%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
 
5.1%
교 20
 
4.6%
상 19
 
4.4%
터 17
 
3.9%
대 16
 
3.7%
도 14
 
3.2%
구 11
 
2.5%
지 11
 
2.5%
층 11
 
2.5%
청 10
 
2.3%
Other values (118) 284
65.3%
Decimal Number
ValueCountFrequency (%)
2 9
56.2%
1 3
 
18.8%
3 2
 
12.5%
5 1
 
6.2%
4 1
 
6.2%
Space Separator
ValueCountFrequency (%)
58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
# 2
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 435
82.5%
Common 92
 
17.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
 
5.1%
교 20
 
4.6%
상 19
 
4.4%
터 17
 
3.9%
대 16
 
3.7%
도 14
 
3.2%
구 11
 
2.5%
지 11
 
2.5%
층 11
 
2.5%
청 10
 
2.3%
Other values (118) 284
65.3%
Common
ValueCountFrequency (%)
58
63.0%
2 9
 
9.8%
( 6
 
6.5%
) 6
 
6.5%
- 3
 
3.3%
1 3
 
3.3%
# 2
 
2.2%
3 2
 
2.2%
> 1
 
1.1%
5 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 435
82.5%
ASCII 92
 
17.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58
63.0%
2 9
 
9.8%
( 6
 
6.5%
) 6
 
6.5%
- 3
 
3.3%
1 3
 
3.3%
# 2
 
2.2%
3 2
 
2.2%
> 1
 
1.1%
5 1
 
1.1%
Hangul
ValueCountFrequency (%)
동 22
 
5.1%
교 20
 
4.6%
상 19
 
4.4%
터 17
 
3.9%
대 16
 
3.7%
도 14
 
3.2%
구 11
 
2.5%
지 11
 
2.5%
층 11
 
2.5%
청 10
 
2.3%
Other values (118) 284
65.3%

운영기관
Categorical

Distinct11
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Memory size668.0 B
동구청
15 
서구청
14 
유성구청
13 
중구청
10 
대덕구청
7 
Other values (6)
8 

Length

Max length7
Median length3
Mean length3.6865672
Min length3

Unique

Unique5 ?
Unique (%)7.5%

Sample

1st row동구청
2nd row서구청
3rd row서구청
4th row서구청
5th row유성구청

Common Values

ValueCountFrequency (%)
동구청 15
22.4%
서구청 14
20.9%
유성구청 13
19.4%
중구청 10
14.9%
대덕구청 7
10.4%
대전광역시청 3
 
4.5%
대전 서구청 1
 
1.5%
대전 유성구청 1
 
1.5%
대전 중구청 1
 
1.5%
대전 대덕구청 1
 
1.5%

Length

2023-12-12T22:00:25.633389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동구청 16
22.2%
서구청 15
20.8%
유성구청 14
19.4%
중구청 11
15.3%
대덕구청 8
11.1%
대전 5
 
6.9%
대전광역시청 3
 
4.2%

설치장소
Text

Distinct63
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-12T22:00:25.949358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length27
Mean length18.58209
Min length16

Characters and Unicode

Total characters1245
Distinct characters89
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)88.1%

Sample

1st row대전광역시 동구 정동 1-281
2nd row대전광역시 서구 둔산동 2168
3rd row대전광역시 서구 갈마동 1459-1
4th row대전광역시 서구 변동 산10-65
5th row대전광역시 유성구 봉산동 산31-1
ValueCountFrequency (%)
대전광역시 67
24.2%
서구 16
 
5.8%
동구 16
 
5.8%
유성구 16
 
5.8%
중구 11
 
4.0%
대덕구 8
 
2.9%
흑석동 4
 
1.4%
100 3
 
1.1%
침산동 3
 
1.1%
방동 2
 
0.7%
Other values (113) 131
47.3%
2023-12-12T22:00:26.429622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
212
17.0%
대 79
 
6.3%
동 78
 
6.3%
구 73
 
5.9%
전 71
 
5.7%
광 67
 
5.4%
역 67
 
5.4%
시 67
 
5.4%
1 61
 
4.9%
- 42
 
3.4%
Other values (79) 428
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 732
58.8%
Decimal Number 250
 
20.1%
Space Separator 212
 
17.0%
Dash Punctuation 42
 
3.4%
Close Punctuation 4
 
0.3%
Open Punctuation 4
 
0.3%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 79
10.8%
동 78
10.7%
구 73
10.0%
전 71
 
9.7%
광 67
 
9.2%
역 67
 
9.2%
시 67
 
9.2%
성 18
 
2.5%
서 17
 
2.3%
산 16
 
2.2%
Other values (64) 179
24.5%
Decimal Number
ValueCountFrequency (%)
1 61
24.4%
0 30
12.0%
3 28
11.2%
5 27
10.8%
2 26
10.4%
4 22
 
8.8%
8 16
 
6.4%
6 15
 
6.0%
9 14
 
5.6%
7 11
 
4.4%
Space Separator
ValueCountFrequency (%)
212
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 732
58.8%
Common 512
41.1%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 79
10.8%
동 78
10.7%
구 73
10.0%
전 71
 
9.7%
광 67
 
9.2%
역 67
 
9.2%
시 67
 
9.2%
성 18
 
2.5%
서 17
 
2.3%
산 16
 
2.2%
Other values (64) 179
24.5%
Common
ValueCountFrequency (%)
212
41.4%
1 61
 
11.9%
- 42
 
8.2%
0 30
 
5.9%
3 28
 
5.5%
5 27
 
5.3%
2 26
 
5.1%
4 22
 
4.3%
8 16
 
3.1%
6 15
 
2.9%
Other values (4) 33
 
6.4%
Latin
ValueCountFrequency (%)
m 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 732
58.8%
ASCII 513
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
212
41.3%
1 61
 
11.9%
- 42
 
8.2%
0 30
 
5.8%
3 28
 
5.5%
5 27
 
5.3%
2 26
 
5.1%
4 22
 
4.3%
8 16
 
3.1%
6 15
 
2.9%
Other values (5) 34
 
6.6%
Hangul
ValueCountFrequency (%)
대 79
10.8%
동 78
10.7%
구 73
10.0%
전 71
 
9.7%
광 67
 
9.2%
역 67
 
9.2%
시 67
 
9.2%
성 18
 
2.5%
서 17
 
2.3%
산 16
 
2.2%
Other values (64) 179
24.5%

위도
Real number (ℝ)

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.324392
Minimum36.200245
Maximum36.461067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T22:00:26.592402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.200245
5-th percentile36.239916
Q136.27855
median36.322083
Q336.35283
95-th percentile36.449482
Maximum36.461067
Range0.260822
Interquartile range (IQR)0.07428

Descriptive statistics

Standard deviation0.058487345
Coefficient of variation (CV)0.0016101397
Kurtosis0.35503625
Mean36.324392
Median Absolute Deviation (MAD)0.038376
Skewness0.4385909
Sum2433.7342
Variance0.0034207695
MonotonicityNot monotonic
2023-12-12T22:00:26.732878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.335535 1
 
1.5%
36.317295 1
 
1.5%
36.325279 1
 
1.5%
36.296769 1
 
1.5%
36.35149 1
 
1.5%
36.440552 1
 
1.5%
36.25585 1
 
1.5%
36.304469 1
 
1.5%
36.281789 1
 
1.5%
36.360459 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
36.200245 1
1.5%
36.200641 1
1.5%
36.228426 1
1.5%
36.238466 1
1.5%
36.2433 1
1.5%
36.25585 1
1.5%
36.26011 1
1.5%
36.26095 1
1.5%
36.2634 1
1.5%
36.268189 1
1.5%
ValueCountFrequency (%)
36.461067 1
1.5%
36.455906 1
1.5%
36.454193 1
1.5%
36.451611 1
1.5%
36.444515 1
1.5%
36.440552 1
1.5%
36.413307 1
1.5%
36.376123 1
1.5%
36.372709 1
1.5%
36.370568 1
1.5%

경도
Real number (ℝ)

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.38823
Minimum127.2329
Maximum127.49282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T22:00:26.909241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.2329
5-th percentile127.28091
Q1127.34645
median127.38669
Q3127.43722
95-th percentile127.46832
Maximum127.49282
Range0.25992
Interquartile range (IQR)0.090769

Descriptive statistics

Standard deviation0.059276385
Coefficient of variation (CV)0.00046532073
Kurtosis-0.17124551
Mean127.38823
Median Absolute Deviation (MAD)0.044052
Skewness-0.49995792
Sum8535.0116
Variance0.0035136898
MonotonicityNot monotonic
2023-12-12T22:00:27.092723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.433715 1
 
1.5%
127.457727 1
 
1.5%
127.421326 1
 
1.5%
127.319249 1
 
1.5%
127.303141 1
 
1.5%
127.38364 1
 
1.5%
127.341606 1
 
1.5%
127.366733 1
 
1.5%
127.467681 1
 
1.5%
127.393235 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
127.2329 1
1.5%
127.252 1
1.5%
127.256 1
1.5%
127.271383 1
1.5%
127.303141 1
1.5%
127.308091 1
1.5%
127.308466 1
1.5%
127.3126 1
1.5%
127.319249 1
1.5%
127.333112 1
1.5%
ValueCountFrequency (%)
127.49282 1
1.5%
127.48068 1
1.5%
127.472672 1
1.5%
127.468589 1
1.5%
127.467681 1
1.5%
127.464372 1
1.5%
127.463967 1
1.5%
127.458327 1
1.5%
127.457727 1
1.5%
127.4548 1
1.5%

구축년월
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.3839
Minimum2009
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T22:00:27.237499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2010
Q12015
median2015
Q32017
95-th percentile2020
Maximum2020
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9375649
Coefficient of variation (CV)0.0014575709
Kurtosis-0.21351055
Mean2015.3839
Median Absolute Deviation (MAD)2
Skewness-0.54492081
Sum135030.72
Variance8.6292877
MonotonicityNot monotonic
2023-12-12T22:00:27.369515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2015.0 24
35.8%
2017.0 12
17.9%
2010.0 9
 
13.4%
2018.12 6
 
9.0%
2020.0 6
 
9.0%
2014.0 4
 
6.0%
2019.0 2
 
3.0%
2018.0 1
 
1.5%
2009.0 1
 
1.5%
2011.0 1
 
1.5%
ValueCountFrequency (%)
2009.0 1
 
1.5%
2010.0 9
 
13.4%
2011.0 1
 
1.5%
2014.0 4
 
6.0%
2015.0 24
35.8%
2016.0 1
 
1.5%
2017.0 12
17.9%
2018.0 1
 
1.5%
2018.12 6
 
9.0%
2019.0 2
 
3.0%
ValueCountFrequency (%)
2020.0 6
 
9.0%
2019.0 2
 
3.0%
2018.12 6
 
9.0%
2018.0 1
 
1.5%
2017.0 12
17.9%
2016.0 1
 
1.5%
2015.0 24
35.8%
2014.0 4
 
6.0%
2011.0 1
 
1.5%
2010.0 9
 
13.4%

Interactions

2023-12-12T22:00:22.168000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.021990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.427082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.805510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:22.264837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.130516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.524315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.900067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:22.360272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.234456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.631487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.985267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:22.471463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.322247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:21.714962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:00:22.079900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:00:27.455547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분용 도관리번호세부 지점명운영기관설치장소위도경도구축년월
순번1.0000.9330.9000.7300.8700.4120.9560.1560.5180.871
구분0.9331.0001.0000.6420.0000.2680.8050.0000.2440.820
용 도0.9001.0001.0000.7920.0000.4320.7580.0000.4690.795
관리번호0.7300.6420.7921.0000.9680.0000.9800.0000.7920.931
세부 지점명0.8700.0000.0000.9681.0001.0000.9981.0000.9880.916
운영기관0.4120.2680.4320.0001.0001.0001.0000.4760.7410.357
설치장소0.9560.8050.7580.9800.9981.0001.0001.0001.0000.990
위도0.1560.0000.0000.0001.0000.4761.0001.0000.4720.481
경도0.5180.2440.4690.7920.9880.7411.0000.4721.0000.106
구축년월0.8710.8200.7950.9310.9160.3570.9900.4810.1061.000
2023-12-12T22:00:27.587987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용 도운영기관구분
용 도1.0000.2020.952
운영기관0.2021.0000.143
구분0.9520.1431.000
2023-12-12T22:00:27.679996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위도경도구축년월구분용 도운영기관
순번1.000-0.085-0.157-0.4660.8600.6940.180
위도-0.0851.000-0.0090.0310.0000.0000.229
경도-0.157-0.0091.0000.0090.1340.2280.426
구축년월-0.4660.0310.0091.0000.7270.5470.185
구분0.8600.0000.1340.7271.0000.9520.143
용 도0.6940.0000.2280.5470.9521.0000.202
운영기관0.1800.2290.4260.1850.1430.2021.000

Missing values

2023-12-12T22:00:22.581915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:00:22.758967image/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

순번구분용 도관리번호세부 지점명운영기관설치장소위도경도구축년월
01재난방송 중계설비라디오, DMB동구-05-18001대전역지하차도동구청대전광역시 동구 정동 1-28136.335535127.4337152018.12
12재난방송 중계설비라디오, DMB서구-05-18001삼천지하차도서구청대전광역시 서구 둔산동 216836.360459127.3932352018.12
23재난방송 중계설비라디오, DMB서구-05-18002갈마지하차도서구청대전광역시 서구 갈마동 1459-136.34748127.3774062018.12
34재난방송 중계설비라디오, DMB서구-05-18003도솔터널서구청대전광역시 서구 변동 산10-6536.333915127.3670562018.12
45재난방송 중계설비라디오, DMB유성구-05-18001오봉터널유성구청대전광역시 유성구 봉산동 산31-136.444515127.3787362018.12
56재난방송 중계설비라디오, DMB유성구-04-20002둔곡터널유성구청대전광역시 유성구 둔곡동 1459-136.461067127.3706822018.12
67재해문자전광판하천, 일반서구-03-18001상보안유원지(흑석)서구청대전광역시 서구 흑석동 73836.276074127.3436472018.0
78재해문자전광판하천, 일반대덕구-03-15002오정동대덕구청대전광역시 대덕구 오정동 461-3번지 (계단옆)36.350155127.4051152015.0
89재해문자전광판일반동구-03-15002비룡램프동구청대전광역시 동구 비룡동 335-1 번지(램프 출구 지나 50m 전방)36.351064127.4726722015.0
910재해문자전광판하천, 일반동구-03-15003상소동오토캠핑장 입구동구청대전광역시 동구 삼괴동 854-236.238466127.4685892015.0
순번구분용 도관리번호세부 지점명운영기관설치장소위도경도구축년월
5758기상관측장비적설결빙계유성구-04-15005노은터널유성구청대전광역시 유성구 구암동 51436.365569127.3080912015.0
5859기상관측장비시정계동구-04-15002신상교 다리 위동구청대전광역시 동구 신상동 353-136.354171127.492822015.0
5960기상관측장비지진계측기서구-04-14001대전광역시청대전광역시청대전광역시 서구 둔산로 10036.3504127.38452014.0
6061기상관측장비지진계측기서구-04-15001대전 서구청대전 서구청대전광역시 서구 둔산서로 10036.3554127.38372015.0
6162기상관측장비지진계측기유성구-04-20002대전 유성구청대전 유성구청대전광역시 유성구 대학로 21136.3623127.2562014.0
6263기상관측장비지진계측기중구-04-14004대전 중구청대전 중구청대전광역시 중구 중앙로 10036.3253127.42122015.0
6364기상관측장비지진계측기대덕구-02-15005대전 대덕구청대전 대덕구청대전광역시 대덕구 대전로 1033번길 2036.3467127.41552015.0
6465기상관측장비지진계측기동구-04-15001대전 동구청대전 동구청대전광역시 동구 동구청로 14736.3118127.45482015.0
6566기상관측장비지진계측기유성구-04-15001한빛대교대전광역시청대전광역시 유성구 전민동 11-136.2433127.2522015.0
6667기상관측장비지진계측기유성구-04-16001문평대교대전광역시청대전광역시 유성구 봉산동 32936.2634127.23292016.0