Overview

Dataset statistics

Number of variables16
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory136.5 B

Variable types

Numeric5
Categorical8
Text3

Dataset

Description대전광역시 서구 관내에서 운영중인 재난예경보시설의 현황 정보(용도, 운영기관, 행정동, 위경도 등)를 제공합니다.
URLhttps://www.data.go.kr/data/15104090/fileData.do

Alerts

구분 has constant value ""Constant
운영기관 has constant value ""Constant
통신방식 has constant value ""Constant
행정동코드 is highly overall correlated with 법정동코드 and 4 other fieldsHigh correlation
행정동 is highly overall correlated with 법정동코드 and 4 other fieldsHigh correlation
순번 is highly overall correlated with 용도 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정동 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 행정동 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 행정동 and 2 other fieldsHigh correlation
구축년 is highly overall correlated with 용도 and 1 other fieldsHigh correlation
용도 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
법정동 is highly overall correlated with 법정동코드 and 4 other fieldsHigh correlation
모델명 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
행정동 is highly imbalanced (51.6%)Imbalance
행정동코드 is highly imbalanced (51.6%)Imbalance
순번 has unique valuesUnique
관리번호 has unique valuesUnique
세부지점명 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:43:09.834480
Analysis finished2023-12-12 19:43:13.606994
Duration3.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.5
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:43:13.703285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.55
Q113.75
median26.5
Q339.25
95-th percentile49.45
Maximum52
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.57187763
Kurtosis-1.2
Mean26.5
Median Absolute Deviation (MAD)13
Skewness0
Sum1378
Variance229.66667
MonotonicityStrictly increasing
2023-12-13T04:43:14.134308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
28 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%
43 1
1.9%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
재난방송
52 

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 52
100.0%

Length

2023-12-13T04:43:14.271196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:43:14.354495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재난방송 52
100.0%

용도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
단독형
27 
실내형
24 
실외형
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
단독형 27
51.9%
실내형 24
46.2%
실외형 1
 
1.9%

Length

2023-12-13T04:43:14.449216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:43:14.544760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독형 27
51.9%
실내형 24
46.2%
실외형 1
 
1.9%

관리번호
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-13T04:43:14.734966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters572
Distinct characters13
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

Unique52 ?
Unique (%)100.0%

Sample

1st row서구-02-18003
2nd row서구-02-18004
3rd row서구-02-18005
4th row서구-02-18006
5th row서구-02-18007
ValueCountFrequency (%)
서구-02-18003 1
 
1.9%
서구-02-18004 1
 
1.9%
서구-02-17020 1
 
1.9%
서구-02-17010 1
 
1.9%
서구-02-17011 1
 
1.9%
서구-02-17012 1
 
1.9%
서구-02-17013 1
 
1.9%
서구-02-17014 1
 
1.9%
서구-02-17015 1
 
1.9%
서구-02-17016 1
 
1.9%
Other values (42) 42
80.8%
2023-12-13T04:43:15.049878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 148
25.9%
- 104
18.2%
2 73
12.8%
1 68
11.9%
서 52
 
9.1%
구 52
 
9.1%
7 21
 
3.7%
8 17
 
3.0%
3 9
 
1.6%
5 9
 
1.6%
Other values (3) 19
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 364
63.6%
Dash Punctuation 104
 
18.2%
Other Letter 104
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 148
40.7%
2 73
20.1%
1 68
18.7%
7 21
 
5.8%
8 17
 
4.7%
3 9
 
2.5%
5 9
 
2.5%
6 7
 
1.9%
4 6
 
1.6%
9 6
 
1.6%
Other Letter
ValueCountFrequency (%)
서 52
50.0%
구 52
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 468
81.8%
Hangul 104
 
18.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 148
31.6%
- 104
22.2%
2 73
15.6%
1 68
14.5%
7 21
 
4.5%
8 17
 
3.6%
3 9
 
1.9%
5 9
 
1.9%
6 7
 
1.5%
4 6
 
1.3%
Hangul
ValueCountFrequency (%)
서 52
50.0%
구 52
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468
81.8%
Hangul 104
 
18.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 148
31.6%
- 104
22.2%
2 73
15.6%
1 68
14.5%
7 21
 
4.5%
8 17
 
3.6%
3 9
 
1.9%
5 9
 
1.9%
6 7
 
1.5%
4 6
 
1.3%
Hangul
ValueCountFrequency (%)
서 52
50.0%
구 52
50.0%

세부지점명
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-13T04:43:15.312718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.0576923
Min length3

Characters and Unicode

Total characters419
Distinct characters101
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

Unique52 ?
Unique (%)100.0%

Sample

1st row평촌3동 마을회관
2nd row평촌1동 마을회관
3rd row정림동 명암경로당
4th row정림동 주민센터
5th row용촌동 미림경로당
ValueCountFrequency (%)
노인회관 7
 
7.6%
경로당 5
 
5.4%
마을회관 5
 
5.4%
원정동 3
 
3.3%
정림동 2
 
2.2%
주민센터 2
 
2.2%
흑석동 2
 
2.2%
상류 2
 
2.2%
평촌3동 1
 
1.1%
평촌2동 1
 
1.1%
Other values (62) 62
67.4%
2023-12-13T04:43:15.686607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
 
10.0%
동 31
 
7.4%
관 15
 
3.6%
원 15
 
3.6%
회 15
 
3.6%
1 13
 
3.1%
정 12
 
2.9%
노 11
 
2.6%
2 10
 
2.4%
을 9
 
2.1%
Other values (91) 246
58.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 332
79.2%
Space Separator 42
 
10.0%
Decimal Number 31
 
7.4%
Open Punctuation 7
 
1.7%
Close Punctuation 7
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 31
 
9.3%
관 15
 
4.5%
원 15
 
4.5%
회 15
 
4.5%
정 12
 
3.6%
노 11
 
3.3%
을 9
 
2.7%
마 9
 
2.7%
통 8
 
2.4%
지 8
 
2.4%
Other values (82) 199
59.9%
Decimal Number
ValueCountFrequency (%)
1 13
41.9%
2 10
32.3%
3 4
 
12.9%
7 2
 
6.5%
9 1
 
3.2%
8 1
 
3.2%
Space Separator
ValueCountFrequency (%)
42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 332
79.2%
Common 87
 
20.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 31
 
9.3%
관 15
 
4.5%
원 15
 
4.5%
회 15
 
4.5%
정 12
 
3.6%
노 11
 
3.3%
을 9
 
2.7%
마 9
 
2.7%
통 8
 
2.4%
지 8
 
2.4%
Other values (82) 199
59.9%
Common
ValueCountFrequency (%)
42
48.3%
1 13
 
14.9%
2 10
 
11.5%
( 7
 
8.0%
) 7
 
8.0%
3 4
 
4.6%
7 2
 
2.3%
9 1
 
1.1%
8 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 332
79.2%
ASCII 87
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42
48.3%
1 13
 
14.9%
2 10
 
11.5%
( 7
 
8.0%
) 7
 
8.0%
3 4
 
4.6%
7 2
 
2.3%
9 1
 
1.1%
8 1
 
1.1%
Hangul
ValueCountFrequency (%)
동 31
 
9.3%
관 15
 
4.5%
원 15
 
4.5%
회 15
 
4.5%
정 12
 
3.6%
노 11
 
3.3%
을 9
 
2.7%
마 9
 
2.7%
통 8
 
2.4%
지 8
 
2.4%
Other values (82) 199
59.9%

운영기관
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
서구
52 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 52
100.0%

Length

2023-12-13T04:43:15.823533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:43:15.934610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 52
100.0%

행정동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
기성동
42 
가수원동
5 
정림동
 
4
만년동
 
1

Length

Max length4
Median length3
Mean length3.0961538
Min length3

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row기성동
2nd row기성동
3rd row정림동
4th row정림동
5th row기성동

Common Values

ValueCountFrequency (%)
기성동 42
80.8%
가수원동 5
 
9.6%
정림동 4
 
7.7%
만년동 1
 
1.9%

Length

2023-12-13T04:43:16.042308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:43:16.194335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기성동 42
80.8%
가수원동 5
 
9.6%
정림동 4
 
7.7%
만년동 1
 
1.9%

행정동코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
3017060000
42 
3017059000
5 
3017053500
 
4
3017065000
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row3017060000
2nd row3017060000
3rd row3017053500
4th row3017053500
5th row3017060000

Common Values

ValueCountFrequency (%)
3017060000 42
80.8%
3017059000 5
 
9.6%
3017053500 4
 
7.7%
3017065000 1
 
1.9%

Length

2023-12-13T04:43:16.337457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:43:16.483335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3017060000 42
80.8%
3017059000 5
 
9.6%
3017053500 4
 
7.7%
3017065000 1
 
1.9%

법정동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
흑석동
12 
원정동
8 
괴곡동
4 
장안동
4 
평촌동
3 
Other values (9)
21 

Length

Max length4
Median length3
Mean length3.0384615
Min length2

Unique

Unique2 ?
Unique (%)3.8%

Sample

1st row평촌동
2nd row평촌동
3rd row정림동
4th row정림동
5th row용촌동

Common Values

ValueCountFrequency (%)
흑석동 12
23.1%
원정동 8
15.4%
괴곡동 4
 
7.7%
장안동 4
 
7.7%
평촌동 3
 
5.8%
용촌동 3
 
5.8%
산직동 3
 
5.8%
매노동 3
 
5.8%
가수원동 3
 
5.8%
봉곡동 3
 
5.8%
Other values (4) 6
11.5%

Length

2023-12-13T04:43:16.658170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
흑석동 12
23.1%
원정동 8
15.4%
괴곡동 4
 
7.7%
장안동 4
 
7.7%
평촌동 3
 
5.8%
용촌동 3
 
5.8%
산직동 3
 
5.8%
매노동 3
 
5.8%
가수원동 3
 
5.8%
봉곡동 3
 
5.8%
Other values (4) 6
11.5%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.017012 × 109
Minimum3.0170104 × 109
Maximum3.0170128 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:43:16.791779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0170104 × 109
5-th percentile3.0170114 × 109
Q13.0170117 × 109
median3.017012 × 109
Q33.0170124 × 109
95-th percentile3.0170127 × 109
Maximum3.0170128 × 109
Range2400
Interquartile range (IQR)700

Descriptive statistics

Standard deviation511.33089
Coefficient of variation (CV)1.6948255 × 10-7
Kurtosis2.170966
Mean3.017012 × 109
Median Absolute Deviation (MAD)300
Skewness-1.0702489
Sum1.5688463 × 1011
Variance261459.28
MonotonicityNot monotonic
2023-12-13T04:43:16.921382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3017011700 12
23.1%
3017012400 8
15.4%
3017012700 4
 
7.7%
3017012000 4
 
7.7%
3017012100 3
 
5.8%
3017012500 3
 
5.8%
3017011900 3
 
5.8%
3017011800 3
 
5.8%
3017011400 3
 
5.8%
3017012600 3
 
5.8%
Other values (4) 6
11.5%
ValueCountFrequency (%)
3017010400 2
 
3.8%
3017011400 3
 
5.8%
3017011700 12
23.1%
3017011800 3
 
5.8%
3017011900 3
 
5.8%
3017012000 4
 
7.7%
3017012100 3
 
5.8%
3017012200 1
 
1.9%
3017012300 2
 
3.8%
3017012400 8
15.4%
ValueCountFrequency (%)
3017012800 1
 
1.9%
3017012700 4
7.7%
3017012600 3
 
5.8%
3017012500 3
 
5.8%
3017012400 8
15.4%
3017012300 2
 
3.8%
3017012200 1
 
1.9%
3017012100 3
 
5.8%
3017012000 4
7.7%
3017011900 3
 
5.8%

지번주소
Text

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-13T04:43:17.195738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length18
Min length15

Characters and Unicode

Total characters936
Distinct characters57
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

Unique50 ?
Unique (%)96.2%

Sample

1st row대전광역시 서구 평촌동 830-2
2nd row대전광역시 서구 평촌동 171-4
3rd row대전광역시 서구 정림동 250-1
4th row대전광역시 서구 정림동 636
5th row대전광역시 서구 용촌동 330-1
ValueCountFrequency (%)
대전광역시 52
24.5%
서구 52
24.5%
흑석동 12
 
5.7%
원정동 8
 
3.8%
괴곡동 5
 
2.4%
장안동 4
 
1.9%
산직동 3
 
1.4%
평촌동 3
 
1.4%
매노동 3
 
1.4%
봉곡동 3
 
1.4%
Other values (61) 67
31.6%
2023-12-13T04:43:17.664828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164
17.5%
대 52
 
5.6%
광 52
 
5.6%
역 52
 
5.6%
시 52
 
5.6%
서 52
 
5.6%
구 52
 
5.6%
전 52
 
5.6%
동 52
 
5.6%
1 39
 
4.2%
Other values (47) 317
33.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 549
58.7%
Decimal Number 191
 
20.4%
Space Separator 164
 
17.5%
Dash Punctuation 30
 
3.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 52
9.5%
광 52
9.5%
역 52
9.5%
시 52
9.5%
서 52
9.5%
구 52
9.5%
전 52
9.5%
동 52
9.5%
석 13
 
2.4%
흑 13
 
2.4%
Other values (33) 107
19.5%
Decimal Number
ValueCountFrequency (%)
1 39
20.4%
2 30
15.7%
3 23
12.0%
5 19
9.9%
0 17
8.9%
8 14
 
7.3%
4 13
 
6.8%
6 13
 
6.8%
7 12
 
6.3%
9 11
 
5.8%
Space Separator
ValueCountFrequency (%)
164
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 549
58.7%
Common 387
41.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 52
9.5%
광 52
9.5%
역 52
9.5%
시 52
9.5%
서 52
9.5%
구 52
9.5%
전 52
9.5%
동 52
9.5%
석 13
 
2.4%
흑 13
 
2.4%
Other values (33) 107
19.5%
Common
ValueCountFrequency (%)
164
42.4%
1 39
 
10.1%
- 30
 
7.8%
2 30
 
7.8%
3 23
 
5.9%
5 19
 
4.9%
0 17
 
4.4%
8 14
 
3.6%
4 13
 
3.4%
6 13
 
3.4%
Other values (4) 25
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 549
58.7%
ASCII 387
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164
42.4%
1 39
 
10.1%
- 30
 
7.8%
2 30
 
7.8%
3 23
 
5.9%
5 19
 
4.9%
0 17
 
4.4%
8 14
 
3.6%
4 13
 
3.4%
6 13
 
3.4%
Other values (4) 25
 
6.5%
Hangul
ValueCountFrequency (%)
대 52
9.5%
광 52
9.5%
역 52
9.5%
시 52
9.5%
서 52
9.5%
구 52
9.5%
전 52
9.5%
동 52
9.5%
석 13
 
2.4%
흑 13
 
2.4%
Other values (33) 107
19.5%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.261219
Minimum36.213678
Maximum36.366418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:43:17.839789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.213678
5-th percentile36.223981
Q136.239212
median36.25921
Q336.275917
95-th percentile36.30485
Maximum36.366418
Range0.1527398
Interquartile range (IQR)0.03670475

Descriptive statistics

Standard deviation0.026983599
Coefficient of variation (CV)0.00074414484
Kurtosis3.3890403
Mean36.261219
Median Absolute Deviation (MAD)0.0169615
Skewness1.1363641
Sum1885.5834
Variance0.00072811461
MonotonicityNot monotonic
2023-12-13T04:43:17.999053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.239297 1
 
1.9%
36.26011 1
 
1.9%
36.278116 1
 
1.9%
36.255186 1
 
1.9%
36.275486 1
 
1.9%
36.237806 1
 
1.9%
36.25714 1
 
1.9%
36.2579 1
 
1.9%
36.213678 1
 
1.9%
36.238033 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
36.213678 1
1.9%
36.213995 1
1.9%
36.221844 1
1.9%
36.2257298 1
1.9%
36.229572 1
1.9%
36.232248 1
1.9%
36.235269 1
1.9%
36.236181 1
1.9%
36.237806 1
1.9%
36.238033 1
1.9%
ValueCountFrequency (%)
36.3664178 1
1.9%
36.311356 1
1.9%
36.3053599 1
1.9%
36.304433 1
1.9%
36.29887 1
1.9%
36.289583 1
1.9%
36.28509 1
1.9%
36.280161 1
1.9%
36.279161 1
1.9%
36.278116 1
1.9%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.33247
Minimum127.28264
Maximum127.39447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:43:18.164452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.28264
5-th percentile127.29534
Q1127.31649
median127.3374
Q3127.34725
95-th percentile127.36335
Maximum127.39447
Range0.111833
Interquartile range (IQR)0.030759

Descriptive statistics

Standard deviation0.02314681
Coefficient of variation (CV)0.00018178245
Kurtosis-0.084743756
Mean127.33247
Median Absolute Deviation (MAD)0.015149
Skewness-0.11065865
Sum6621.2886
Variance0.00053577479
MonotonicityNot monotonic
2023-12-13T04:43:18.325341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.31533 1
 
1.9%
127.337631 1
 
1.9%
127.334019 1
 
1.9%
127.324763 1
 
1.9%
127.320519 1
 
1.9%
127.286536 1
 
1.9%
127.30627 1
 
1.9%
127.301436 1
 
1.9%
127.336861 1
 
1.9%
127.31365 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
127.282639 1
1.9%
127.286536 1
1.9%
127.29452 1
1.9%
127.296014 1
1.9%
127.298677 1
1.9%
127.301274 1
1.9%
127.301436 1
1.9%
127.302233 1
1.9%
127.30627 1
1.9%
127.309792 1
1.9%
ValueCountFrequency (%)
127.394472 1
1.9%
127.367286 1
1.9%
127.366722 1
1.9%
127.360589 1
1.9%
127.359616 1
1.9%
127.358472 1
1.9%
127.355942 1
1.9%
127.352827 1
1.9%
127.352486 1
1.9%
127.352029 1
1.9%

구축년
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.7115
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:43:18.460117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12017
median2017
Q32018
95-th percentile2020.9
Maximum2022
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6608117
Coefficient of variation (CV)0.00082311654
Kurtosis0.943194
Mean2017.7115
Median Absolute Deviation (MAD)1
Skewness0.85461014
Sum104921
Variance2.7582956
MonotonicityNot monotonic
2023-12-13T04:43:18.569277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2017 18
34.6%
2018 14
26.9%
2016 5
 
9.6%
2020 5
 
9.6%
2015 4
 
7.7%
2019 3
 
5.8%
2022 3
 
5.8%
ValueCountFrequency (%)
2015 4
 
7.7%
2016 5
 
9.6%
2017 18
34.6%
2018 14
26.9%
2019 3
 
5.8%
2020 5
 
9.6%
2022 3
 
5.8%
ValueCountFrequency (%)
2022 3
 
5.8%
2020 5
 
9.6%
2019 3
 
5.8%
2018 14
26.9%
2017 18
34.6%
2016 5
 
9.6%
2015 4
 
7.7%

모델명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
BWS-2200
15 
BWS-2200S
13 
BWS-2204
10 
EB1
10 
SGA-4000
2 

Length

Max length9
Median length8
Mean length7.2884615
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBWS-2200S
2nd rowBWS-2200S
3rd rowBWS-2200S
4th rowBWS-2200S
5th rowBWS-2200S

Common Values

ValueCountFrequency (%)
BWS-2200 15
28.8%
BWS-2200S 13
25.0%
BWS-2204 10
19.2%
EB1 10
19.2%
SGA-4000 2
 
3.8%
OAE-TVAR 2
 
3.8%

Length

2023-12-13T04:43:18.700360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:43:18.836142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bws-2200 15
28.8%
bws-2200s 13
25.0%
bws-2204 10
19.2%
eb1 10
19.2%
sga-4000 2
 
3.8%
oae-tvar 2
 
3.8%

통신방식
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
무선통신
52 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무선통신 52
100.0%

Length

2023-12-13T04:43:18.995440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:43:19.106863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무선통신 52
100.0%

Interactions

2023-12-13T04:43:12.680288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:10.686208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.201907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.703088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:12.155340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:12.827036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:10.783982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.288300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.798061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:12.244412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:12.939502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:10.883414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.371083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.882251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:12.375817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:13.063666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.001010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.474853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.972492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:12.493109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:13.187574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.101371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:11.583543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:12.067375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:12.579718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:43:19.206901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번용도관리번호세부지점명행정동행정동코드법정동법정동코드지번주소위도경도구축년모델명
순번1.0000.7591.0001.0000.3680.3680.7670.4711.0000.4910.2710.9110.878
용도0.7591.0001.0001.0000.2210.2210.5990.4001.0000.0000.1540.8370.842
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
세부지점명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동0.3680.2211.0001.0001.0001.0000.9630.6901.0000.9470.8470.5730.284
행정동코드0.3680.2211.0001.0001.0001.0000.9630.6901.0000.9470.8470.5730.284
법정동0.7670.5991.0001.0000.9630.9631.0001.0001.0000.8850.8520.5170.694
법정동코드0.4710.4001.0001.0000.6900.6901.0001.0001.0000.7840.7000.2930.424
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0000.9661.0001.0001.000
위도0.4910.0001.0001.0000.9470.9470.8850.7840.9661.0000.5950.4930.000
경도0.2710.1541.0001.0000.8470.8470.8520.7001.0000.5951.0000.3860.404
구축년0.9110.8371.0001.0000.5730.5730.5170.2931.0000.4930.3861.0000.766
모델명0.8780.8421.0001.0000.2840.2840.6940.4241.0000.0000.4040.7661.000
2023-12-13T04:43:19.403177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드법정동용도모델명행정동
행정동코드1.0000.7950.2050.1771.000
법정동0.7951.0000.3580.3870.795
용도0.2050.3581.0000.5150.205
모델명0.1770.3870.5151.0000.177
행정동1.0000.7950.2050.1771.000
2023-12-13T04:43:19.529434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정동코드위도경도구축년용도행정동행정동코드법정동모델명
순번1.000-0.0260.144-0.1810.3820.5840.2050.2050.4150.680
법정동코드-0.0261.000-0.129-0.344-0.0410.1820.6110.6110.9190.179
위도0.144-0.1291.0000.4420.2690.0000.6680.6680.6110.000
경도-0.181-0.3440.4421.0000.0390.0300.6920.6920.5430.199
구축년0.382-0.0410.2690.0391.0000.5070.3860.3860.3010.631
용도0.5840.1820.0000.0300.5071.0000.2050.2050.3580.515
행정동0.2050.6110.6680.6920.3860.2051.0001.0000.7950.177
행정동코드0.2050.6110.6680.6920.3860.2051.0001.0000.7950.177
법정동0.4150.9190.6110.5430.3010.3580.7950.7951.0000.387
모델명0.6800.1790.0000.1990.6310.5150.1770.1770.3871.000

Missing values

2023-12-13T04:43:13.337591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:43:13.535118image/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

순번구분용도관리번호세부지점명운영기관행정동행정동코드법정동법정동코드지번주소위도경도구축년모델명통신방식
01재난방송실내형서구-02-18003평촌3동 마을회관서구기성동3017060000평촌동3017012100대전광역시 서구 평촌동 830-236.239297127.315332018BWS-2200S무선통신
12재난방송실내형서구-02-18004평촌1동 마을회관서구기성동3017060000평촌동3017012100대전광역시 서구 평촌동 171-436.238944127.3184722018BWS-2200S무선통신
23재난방송실내형서구-02-18005정림동 명암경로당서구정림동3017053500정림동3017010400대전광역시 서구 정림동 250-136.311356127.3584722018BWS-2200S무선통신
34재난방송실내형서구-02-18006정림동 주민센터서구정림동3017053500정림동3017010400대전광역시 서구 정림동 63636.304433127.3667222018BWS-2200S무선통신
45재난방송실내형서구-02-18007용촌동 미림경로당서구기성동3017060000용촌동3017012500대전광역시 서구 용촌동 330-136.272706127.3104222018BWS-2200S무선통신
56재난방송실내형서구-02-18008산직2동 마을회관서구기성동3017060000산직동3017011900대전광역시 서구 산직동 79-336.235269127.3443362018BWS-2200S무선통신
67재난방송실내형서구-02-18009원정림경로당서구정림동3017053500괴곡동3017012700대전광역시 서구 괴곡동 325-236.289583127.3596162018BWS-2200S무선통신
78재난방송실내형서구-02-18010선골마을회관서구정림동3017053500괴곡동3017012700대전광역시 서구 괴곡동 169-336.276269127.3672862018BWS-2200S무선통신
89재난방송실내형서구-02-18011흑석2동 경로당서구기성동3017060000흑석동3017011700대전광역시 서구 흑석동 19136.262306127.3465052018BWS-2200S무선통신
910재난방송실내형서구-02-18012용촌1동 경로당서구기성동3017060000용촌동3017012500대전광역시 서구 용촌동 241-536.247853127.3230442018BWS-2200S무선통신
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4243재난방송단독형서구-02-19002흑석3통서구기성동3017060000흑석동3017011700대전광역시 서구 흑석동 75236.262637127.3410432019BWS-2200S무선통신
4344재난방송단독형서구-02-19003가수원서구가수원동3017059000가수원동3017011400대전광역시 서구 가수원동 30-236.29887127.3605892019EB1무선통신
4445재난방송단독형서구-02-20001흑석동 3통(세점길)서구기성동3017060000흑석동3017011700대전광역시 서구 흑석동 582-836.265673127.3328452020EB1무선통신
4546재난방송단독형서구-02-20002흑석동 2통(등골)서구기성동3017060000흑석동3017011700대전광역시 서구 흑석동 71536.268096127.3520292020EB1무선통신
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4748재난방송단독형서구-02-20004원정동 17통(수국마을)서구기성동3017060000원정동3017012400대전광역시 서구 원정동 106536.254973127.3022332020EB1무선통신
4849재난방송단독형서구-02-20005원정동 19통(구만리)서구기성동3017060000원정동3017012400대전광역시 서구 원정동 109436.263123127.3012742020EB1무선통신
4950재난방송단독형서구-02-22001가수원교 상류 좌안서구가수원동3017059000가수원동3017011400대전광역시 서구 가수원동 706-836.30536127.3490962022EB1무선통신
5051재난방송단독형서구-02-22002둔산대교좌안 하부서구만년동3017065000만년동3017012800대전광역시 서구 만년동 424(주차장부근)36.366418127.3944722022EB1무선통신
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