Overview

Dataset statistics

Number of variables16
Number of observations42
Missing cells19
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory136.1 B

Variable types

Numeric3
Categorical8
Text4
Boolean1

Dataset

Description대구광역시 남구 그늘막에 대한 데이터로 (관리번호, 설차장소명, 지번주소, 설치일시, 그늘막 유형, 전체높이, 펼침지름)의 현황 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15061580/fileData.do

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
보험가입유무 has constant value ""Constant
데이터기준일자 has constant value ""Constant
그늘막 유형 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
전체높이(m) is highly overall correlated with 설치일자 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 읍면동(행정동) and 1 other fieldsHigh correlation
경도 is highly overall correlated with 읍면동(행정동)High correlation
읍면동(행정동) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
설치일자 is highly overall correlated with 그늘막 유형 and 2 other fieldsHigh correlation
펼침지름(m) is highly overall correlated with 설치일자 and 1 other fieldsHigh correlation
도로명주소 has 19 (45.2%) missing valuesMissing
연번 has unique valuesUnique
관리번호 has unique valuesUnique
설치장소명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:46:59.123798
Analysis finished2023-12-12 10:47:01.505589
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:01.589222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.05
Q111.25
median21.5
Q331.75
95-th percentile39.95
Maximum42
Range41
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.267844
Coefficient of variation (CV)0.5705974
Kurtosis-1.2
Mean21.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum903
Variance150.5
MonotonicityStrictly increasing
2023-12-12T19:47:01.781938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 1
 
2.4%
33 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
32 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
42 1
2.4%
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
대구광역시
42 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 42
100.0%

Length

2023-12-12T19:47:01.958958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:02.104757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 42
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
남구
42 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남구 42
100.0%

Length

2023-12-12T19:47:02.277740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:02.409256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남구 42
100.0%

읍면동(행정동)
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size468.0 B
봉덕3동
7 
이천동
6 
봉덕2동
5 
대명10동
5 
대명5동
4 
Other values (6)
15 

Length

Max length5
Median length4
Mean length4.0238095
Min length3

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st row이천동
2nd row이천동
3rd row이천동
4th row이천동
5th row봉덕1동

Common Values

ValueCountFrequency (%)
봉덕3동 7
16.7%
이천동 6
14.3%
봉덕2동 5
11.9%
대명10동 5
11.9%
대명5동 4
9.5%
대명9동 4
9.5%
대명1동 3
7.1%
대명2동 3
7.1%
봉덕1동 2
 
4.8%
대명11동 2
 
4.8%

Length

2023-12-12T19:47:02.586210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
봉덕3동 7
16.7%
이천동 6
14.3%
봉덕2동 5
11.9%
대명10동 5
11.9%
대명5동 4
9.5%
대명9동 4
9.5%
대명1동 3
7.1%
대명2동 3
7.1%
봉덕1동 2
 
4.8%
대명11동 2
 
4.8%

관리번호
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T19:47:02.916890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5714286
Min length5

Characters and Unicode

Total characters234
Distinct characters17
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

Unique42 ?
Unique (%)100.0%

Sample

1st row스마트-8
2nd row스마트-9
3rd row파라솔-16
4th row파라솔-17
5th row스마트-6
ValueCountFrequency (%)
스마트-8 1
 
2.4%
스마트-12 1
 
2.4%
스마트-19 1
 
2.4%
스마트-1 1
 
2.4%
스마트-2 1
 
2.4%
파라솔-10 1
 
2.4%
파라솔-20 1
 
2.4%
파라솔-11 1
 
2.4%
파라솔-12 1
 
2.4%
스마트-10 1
 
2.4%
Other values (32) 32
76.2%
2023-12-12T19:47:03.404924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 42
17.9%
1 25
10.7%
파 22
9.4%
라 22
9.4%
솔 22
9.4%
스 20
8.5%
트 20
8.5%
마 20
8.5%
2 9
 
3.8%
7 4
 
1.7%
Other values (7) 28
12.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126
53.8%
Decimal Number 66
28.2%
Dash Punctuation 42
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25
37.9%
2 9
 
13.6%
7 4
 
6.1%
9 4
 
6.1%
4 4
 
6.1%
3 4
 
6.1%
6 4
 
6.1%
5 4
 
6.1%
8 4
 
6.1%
0 4
 
6.1%
Other Letter
ValueCountFrequency (%)
파 22
17.5%
라 22
17.5%
솔 22
17.5%
스 20
15.9%
트 20
15.9%
마 20
15.9%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126
53.8%
Common 108
46.2%

Most frequent character per script

Common
ValueCountFrequency (%)
- 42
38.9%
1 25
23.1%
2 9
 
8.3%
7 4
 
3.7%
9 4
 
3.7%
4 4
 
3.7%
3 4
 
3.7%
6 4
 
3.7%
5 4
 
3.7%
8 4
 
3.7%
Hangul
ValueCountFrequency (%)
파 22
17.5%
라 22
17.5%
솔 22
17.5%
스 20
15.9%
트 20
15.9%
마 20
15.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126
53.8%
ASCII 108
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 42
38.9%
1 25
23.1%
2 9
 
8.3%
7 4
 
3.7%
9 4
 
3.7%
4 4
 
3.7%
3 4
 
3.7%
6 4
 
3.7%
5 4
 
3.7%
8 4
 
3.7%
Hangul
ValueCountFrequency (%)
파 22
17.5%
라 22
17.5%
솔 22
17.5%
스 20
15.9%
트 20
15.9%
마 20
15.9%

설치장소명
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T19:47:03.682699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length14.071429
Min length5

Characters and Unicode

Total characters591
Distinct characters144
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row대봉교 교통섬
2nd row희망교 교통섬
3rd row건들바위네거리(이천동행복센터 방향)
4th row건들바위네거리(서봉사 방향)
5th row봉명네거리(현대블루핸즈 방향)
ValueCountFrequency (%)
앞 16
 
17.6%
방향 15
 
16.5%
방면 3
 
3.3%
교통섬 3
 
3.3%
희망교 2
 
2.2%
안지랑네거리(동신점보 2
 
2.2%
중동교 2
 
2.2%
2
 
2.2%
앞산네거리 2
 
2.2%
대명역교차로(남대구세무서 1
 
1.1%
Other values (43) 43
47.3%
2023-12-12T19:47:04.118798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
8.3%
( 32
 
5.4%
) 32
 
5.4%
리 27
 
4.6%
거 25
 
4.2%
네 23
 
3.9%
교 21
 
3.6%
대 20
 
3.4%
앞 20
 
3.4%
방 19
 
3.2%
Other values (134) 323
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 466
78.8%
Space Separator 49
 
8.3%
Open Punctuation 32
 
5.4%
Close Punctuation 32
 
5.4%
Decimal Number 5
 
0.8%
Lowercase Letter 3
 
0.5%
Dash Punctuation 2
 
0.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 27
 
5.8%
거 25
 
5.4%
네 23
 
4.9%
교 21
 
4.5%
대 20
 
4.3%
앞 20
 
4.3%
방 19
 
4.1%
향 16
 
3.4%
구 12
 
2.6%
명 10
 
2.1%
Other values (121) 273
58.6%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
2 1
20.0%
3 1
20.0%
9 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
t 1
33.3%
k 1
33.3%
b 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
O 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 466
78.8%
Common 120
 
20.3%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 27
 
5.8%
거 25
 
5.4%
네 23
 
4.9%
교 21
 
4.5%
대 20
 
4.3%
앞 20
 
4.3%
방 19
 
4.1%
향 16
 
3.4%
구 12
 
2.6%
명 10
 
2.1%
Other values (121) 273
58.6%
Common
ValueCountFrequency (%)
49
40.8%
( 32
26.7%
) 32
26.7%
1 2
 
1.7%
- 2
 
1.7%
2 1
 
0.8%
3 1
 
0.8%
9 1
 
0.8%
Latin
ValueCountFrequency (%)
O 1
20.0%
t 1
20.0%
k 1
20.0%
b 1
20.0%
K 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 466
78.8%
ASCII 125
 
21.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49
39.2%
( 32
25.6%
) 32
25.6%
1 2
 
1.6%
- 2
 
1.6%
O 1
 
0.8%
2 1
 
0.8%
t 1
 
0.8%
k 1
 
0.8%
b 1
 
0.8%
Other values (3) 3
 
2.4%
Hangul
ValueCountFrequency (%)
리 27
 
5.8%
거 25
 
5.4%
네 23
 
4.9%
교 21
 
4.5%
대 20
 
4.3%
앞 20
 
4.3%
방 19
 
4.1%
향 16
 
3.4%
구 12
 
2.6%
명 10
 
2.1%
Other values (121) 273
58.6%

지번주소
Text

Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T19:47:04.359881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.47619
Min length16

Characters and Unicode

Total characters818
Distinct characters26
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

Unique38 ?
Unique (%)90.5%

Sample

1st row대구광역시 남구 이천동 121-379
2nd row대구광역시 남구 이천동 559-12
3rd row대구광역시 남구 이천동 392-4
4th row대구광역시 남구 이천동 437-6
5th row대구광역시 남구 봉덕동 496-7
ValueCountFrequency (%)
대구광역시 42
24.9%
남구 42
24.9%
대명동 21
12.4%
봉덕동 14
 
8.3%
이천동 6
 
3.6%
1135-14 2
 
1.2%
780-3 2
 
1.2%
688-1 1
 
0.6%
2680-7 1
 
0.6%
1593-18 1
 
0.6%
Other values (37) 37
21.9%
2023-12-12T19:47:04.800035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
156
19.1%
구 84
 
10.3%
대 64
 
7.8%
광 42
 
5.1%
역 42
 
5.1%
시 42
 
5.1%
남 42
 
5.1%
1 42
 
5.1%
동 41
 
5.0%
- 41
 
5.0%
Other values (16) 222
27.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 421
51.5%
Decimal Number 200
24.4%
Space Separator 156
 
19.1%
Dash Punctuation 41
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 84
20.0%
대 64
15.2%
광 42
10.0%
역 42
10.0%
시 42
10.0%
남 42
10.0%
동 41
9.7%
명 22
 
5.2%
봉 14
 
3.3%
덕 14
 
3.3%
Other values (4) 14
 
3.3%
Decimal Number
ValueCountFrequency (%)
1 42
21.0%
3 31
15.5%
6 21
10.5%
2 19
9.5%
8 17
8.5%
4 17
8.5%
0 15
 
7.5%
7 14
 
7.0%
9 13
 
6.5%
5 11
 
5.5%
Space Separator
ValueCountFrequency (%)
156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 421
51.5%
Common 397
48.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 84
20.0%
대 64
15.2%
광 42
10.0%
역 42
10.0%
시 42
10.0%
남 42
10.0%
동 41
9.7%
명 22
 
5.2%
봉 14
 
3.3%
덕 14
 
3.3%
Other values (4) 14
 
3.3%
Common
ValueCountFrequency (%)
156
39.3%
1 42
 
10.6%
- 41
 
10.3%
3 31
 
7.8%
6 21
 
5.3%
2 19
 
4.8%
8 17
 
4.3%
4 17
 
4.3%
0 15
 
3.8%
7 14
 
3.5%
Other values (2) 24
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 421
51.5%
ASCII 397
48.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156
39.3%
1 42
 
10.6%
- 41
 
10.3%
3 31
 
7.8%
6 21
 
5.3%
2 19
 
4.8%
8 17
 
4.3%
4 17
 
4.3%
0 15
 
3.8%
7 14
 
3.5%
Other values (2) 24
 
6.0%
Hangul
ValueCountFrequency (%)
구 84
20.0%
대 64
15.2%
광 42
10.0%
역 42
10.0%
시 42
10.0%
남 42
10.0%
동 41
9.7%
명 22
 
5.2%
봉 14
 
3.3%
덕 14
 
3.3%
Other values (4) 14
 
3.3%

도로명주소
Text

MISSING 

Distinct22
Distinct (%)95.7%
Missing19
Missing (%)45.2%
Memory size468.0 B
2023-12-12T19:47:05.036718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length22.043478
Min length20

Characters and Unicode

Total characters507
Distinct characters36
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

Unique21 ?
Unique (%)91.3%

Sample

1st row대구광역시 남구 명덕로330, (이천동)
2nd row대구광역시 남구 명덕로262, (이천동)
3rd row대구광역시 남구 이천로159, (이천동)
4th row대구광역시 남구 이천로75, (봉덕동)
5th row대구광역시 남구 이천로46, (봉덕동)
ValueCountFrequency (%)
대구광역시 23
24.5%
남구 23
24.5%
대명동 14
14.9%
봉덕동 6
 
6.4%
이천동 3
 
3.2%
대명로177 2
 
2.1%
동신맨션 2
 
2.1%
대명로125 1
 
1.1%
대명로29 1
 
1.1%
대명로61 1
 
1.1%
Other values (18) 18
19.1%
2023-12-12T19:47:05.424593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
14.0%
대 48
 
9.5%
구 46
 
9.1%
명 25
 
4.9%
, 25
 
4.9%
동 25
 
4.9%
역 23
 
4.5%
시 23
 
4.5%
광 23
 
4.5%
남 23
 
4.5%
Other values (26) 175
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 309
60.9%
Space Separator 71
 
14.0%
Decimal Number 56
 
11.0%
Other Punctuation 25
 
4.9%
Open Punctuation 23
 
4.5%
Close Punctuation 23
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 48
15.5%
구 46
14.9%
명 25
8.1%
동 25
8.1%
역 23
7.4%
시 23
7.4%
광 23
7.4%
남 23
7.4%
로 23
7.4%
천 9
 
2.9%
Other values (12) 41
13.3%
Decimal Number
ValueCountFrequency (%)
1 11
19.6%
2 9
16.1%
3 7
12.5%
6 6
10.7%
7 5
8.9%
5 5
8.9%
9 5
8.9%
0 4
 
7.1%
4 3
 
5.4%
8 1
 
1.8%
Space Separator
ValueCountFrequency (%)
71
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 309
60.9%
Common 198
39.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 48
15.5%
구 46
14.9%
명 25
8.1%
동 25
8.1%
역 23
7.4%
시 23
7.4%
광 23
7.4%
남 23
7.4%
로 23
7.4%
천 9
 
2.9%
Other values (12) 41
13.3%
Common
ValueCountFrequency (%)
71
35.9%
, 25
 
12.6%
( 23
 
11.6%
) 23
 
11.6%
1 11
 
5.6%
2 9
 
4.5%
3 7
 
3.5%
6 6
 
3.0%
7 5
 
2.5%
5 5
 
2.5%
Other values (4) 13
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 309
60.9%
ASCII 198
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
35.9%
, 25
 
12.6%
( 23
 
11.6%
) 23
 
11.6%
1 11
 
5.6%
2 9
 
4.5%
3 7
 
3.5%
6 6
 
3.0%
7 5
 
2.5%
5 5
 
2.5%
Other values (4) 13
 
6.6%
Hangul
ValueCountFrequency (%)
대 48
15.5%
구 46
14.9%
명 25
8.1%
동 25
8.1%
역 23
7.4%
시 23
7.4%
광 23
7.4%
남 23
7.4%
로 23
7.4%
천 9
 
2.9%
Other values (12) 41
13.3%

위도
Real number (ℝ)

Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.843721
Minimum35.832582
Maximum35.856749
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:05.604896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.832582
5-th percentile35.835072
Q135.83943
median35.841861
Q335.846481
95-th percentile35.855574
Maximum35.856749
Range0.02416661
Interquartile range (IQR)0.0070511725

Descriptive statistics

Standard deviation0.006522209
Coefficient of variation (CV)0.00018196238
Kurtosis-0.41292515
Mean35.843721
Median Absolute Deviation (MAD)0.003123545
Skewness0.64289578
Sum1505.4363
Variance4.253921 × 10-5
MonotonicityNot monotonic
2023-12-12T19:47:05.812046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
35.83721026 2
 
4.8%
35.83940741 2
 
4.8%
35.85493819 1
 
2.4%
35.84230872 1
 
2.4%
35.83949631 1
 
2.4%
35.83932377 1
 
2.4%
35.83958762 1
 
2.4%
35.83258227 1
 
2.4%
35.83529283 1
 
2.4%
35.84287842 1
 
2.4%
Other values (30) 30
71.4%
ValueCountFrequency (%)
35.83258227 1
2.4%
35.83447417 1
2.4%
35.83505995 1
2.4%
35.83529283 1
2.4%
35.83719666 1
2.4%
35.83721026 2
4.8%
35.83894303 1
2.4%
35.83932377 1
2.4%
35.83940741 2
4.8%
35.83949631 1
2.4%
ValueCountFrequency (%)
35.85674888 1
2.4%
35.85661806 1
2.4%
35.85558331 1
2.4%
35.85539448 1
2.4%
35.85530093 1
2.4%
35.85493819 1
2.4%
35.85263741 1
2.4%
35.85093195 1
2.4%
35.84909982 1
2.4%
35.84811918 1
2.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.5886
Minimum128.55767
Maximum128.6062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:06.023699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.55767
5-th percentile128.56122
Q1128.5795
median128.5906
Q3128.59842
95-th percentile128.60614
Maximum128.6062
Range0.0485258
Interquartile range (IQR)0.01892615

Descriptive statistics

Standard deviation0.014188242
Coefficient of variation (CV)0.00011033826
Kurtosis-0.5521477
Mean128.5886
Median Absolute Deviation (MAD)0.0108137
Skewness-0.63354698
Sum5400.7214
Variance0.00020130622
MonotonicityNot monotonic
2023-12-12T19:47:06.274724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
128.5576736 2
 
4.8%
128.5771601 2
 
4.8%
128.6061918 1
 
2.4%
128.5975939 1
 
2.4%
128.5641652 1
 
2.4%
128.575809 1
 
2.4%
128.571527 1
 
2.4%
128.5978196 1
 
2.4%
128.5799638 1
 
2.4%
128.6061994 1
 
2.4%
Other values (30) 30
71.4%
ValueCountFrequency (%)
128.5576736 2
4.8%
128.5610619 1
2.4%
128.5641652 1
2.4%
128.5696204 1
2.4%
128.571527 1
2.4%
128.5741218 1
2.4%
128.575809 1
2.4%
128.5771601 2
4.8%
128.5794805 1
2.4%
128.579546 1
2.4%
ValueCountFrequency (%)
128.6061994 1
2.4%
128.6061918 1
2.4%
128.6061651 1
2.4%
128.605687 1
2.4%
128.605199 1
2.4%
128.6051834 1
2.4%
128.6044754 1
2.4%
128.6039663 1
2.4%
128.5985773 1
2.4%
128.5984894 1
2.4%

설치일자
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
2018-08-24
12 
2019-06-18
8 
2021-09-16
7 
2018-07-25
4 
2020-07-03
4 
Other values (4)
7 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)4.8%

Sample

1st row2020-04-21
2nd row2020-04-21
3rd row2018-08-24
4th row2018-08-24
5th row2019-06-18

Common Values

ValueCountFrequency (%)
2018-08-24 12
28.6%
2019-06-18 8
19.0%
2021-09-16 7
16.7%
2018-07-25 4
 
9.5%
2020-07-03 4
 
9.5%
2020-04-21 3
 
7.1%
2020-06-30 2
 
4.8%
2018-09-07 1
 
2.4%
2018-08-25 1
 
2.4%

Length

2023-12-12T19:47:06.483364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:06.707826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-08-24 12
28.6%
2019-06-18 8
19.0%
2021-09-16 7
16.7%
2018-07-25 4
 
9.5%
2020-07-03 4
 
9.5%
2020-04-21 3
 
7.1%
2020-06-30 2
 
4.8%
2018-09-07 1
 
2.4%
2018-08-25 1
 
2.4%

그늘막 유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
고정형
22 
스마트형
20 

Length

Max length4
Median length3
Mean length3.4761905
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row스마트형
2nd row스마트형
3rd row고정형
4th row고정형
5th row스마트형

Common Values

ValueCountFrequency (%)
고정형 22
52.4%
스마트형 20
47.6%

Length

2023-12-12T19:47:06.947211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:07.119105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형 22
52.4%
스마트형 20
47.6%

전체높이(m)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size468.0 B
3.3
22 
3.0
19 
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st row3.0
2nd row3.0
3rd row3.3
4th row3.3
5th row4.0

Common Values

ValueCountFrequency (%)
3.3 22
52.4%
3.0 19
45.2%
4.0 1
 
2.4%

Length

2023-12-12T19:47:07.297325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:07.451235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3.3 22
52.4%
3.0 19
45.2%
4.0 1
 
2.4%

펼침지름(m)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size468.0 B
4.0
29 
4.4
5 
5.4
4 
5.0
 
2
3.8
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 29
69.0%
4.4 5
 
11.9%
5.4 4
 
9.5%
5.0 2
 
4.8%
3.8 2
 
4.8%

Length

2023-12-12T19:47:07.622020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:07.803351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 29
69.0%
4.4 5
 
11.9%
5.4 4
 
9.5%
5.0 2
 
4.8%
3.8 2
 
4.8%

보험가입유무
Boolean

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size174.0 B
True
42 
ValueCountFrequency (%)
True 42
100.0%
2023-12-12T19:47:07.971964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-06-08
42 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-08
2nd row2023-06-08
3rd row2023-06-08
4th row2023-06-08
5th row2023-06-08

Common Values

ValueCountFrequency (%)
2023-06-08 42
100.0%

Length

2023-12-12T19:47:08.134492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:08.298484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-08 42
100.0%

Interactions

2023-12-12T19:47:00.497700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:59.780535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:00.105227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:00.611487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:59.870502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:00.235233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:00.744466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:59.978904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:00.360229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:47:08.415678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동(행정동)관리번호설치장소명지번주소도로명주소위도경도설치일자그늘막 유형전체높이(m)펼침지름(m)
연번1.0000.8261.0001.0000.9780.8970.6850.5940.7730.7700.5640.856
읍면동(행정동)0.8261.0001.0001.0001.0001.0000.7940.8270.7520.3720.6540.000
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설치장소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소0.9781.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소0.8971.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.6850.7941.0001.0001.0001.0001.0000.2270.6810.0000.5420.553
경도0.5940.8271.0001.0001.0001.0000.2271.0000.7190.3960.1620.226
설치일자0.7730.7521.0001.0001.0001.0000.6810.7191.0000.8730.8730.838
그늘막 유형0.7700.3721.0001.0001.0001.0000.0000.3960.8731.0001.0000.484
전체높이(m)0.5640.6541.0001.0001.0001.0000.5420.1620.8731.0001.0000.446
펼침지름(m)0.8560.0001.0001.0001.0001.0000.5530.2260.8380.4840.4461.000
2023-12-12T19:47:08.631937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
그늘막 유형설치일자전체높이(m)펼침지름(m)읍면동(행정동)
그늘막 유형1.0000.8240.9870.5640.306
설치일자0.8241.0000.5460.6430.448
전체높이(m)0.9870.5461.0000.3640.432
펼침지름(m)0.5640.6430.3641.0000.000
읍면동(행정동)0.3060.4480.4320.0001.000
2023-12-12T19:47:08.808241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도읍면동(행정동)설치일자그늘막 유형전체높이(m)펼침지름(m)
연번1.000-0.393-0.2320.5260.4810.5400.3630.482
위도-0.3931.0000.3680.4820.3810.0000.3430.232
경도-0.2320.3681.0000.6090.2990.3160.0000.162
읍면동(행정동)0.5260.4820.6091.0000.4480.3060.4320.000
설치일자0.4810.3810.2990.4481.0000.8240.5460.643
그늘막 유형0.5400.0000.3160.3060.8241.0000.9870.564
전체높이(m)0.3630.3430.0000.4320.5460.9871.0000.364
펼침지름(m)0.4820.2320.1620.0000.6430.5640.3641.000

Missing values

2023-12-12T19:47:00.998694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:47:01.400626image/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

연번시도시군구읍면동(행정동)관리번호설치장소명지번주소도로명주소위도경도설치일자그늘막 유형전체높이(m)펼침지름(m)보험가입유무데이터기준일자
01대구광역시남구이천동스마트-8대봉교 교통섬대구광역시 남구 이천동 121-379대구광역시 남구 명덕로330, (이천동)35.854938128.6061922020-04-21스마트형3.04.0Y2023-06-08
12대구광역시남구이천동스마트-9희망교 교통섬대구광역시 남구 이천동 559-12<NA>35.8491128.6039662020-04-21스마트형3.04.0Y2023-06-08
23대구광역시남구이천동파라솔-16건들바위네거리(이천동행복센터 방향)대구광역시 남구 이천동 392-4대구광역시 남구 명덕로262, (이천동)35.855301128.5985772018-08-24고정형3.34.0Y2023-06-08
34대구광역시남구이천동파라솔-17건들바위네거리(서봉사 방향)대구광역시 남구 이천동 437-6대구광역시 남구 이천로159, (이천동)35.855394128.5978922018-08-24고정형3.34.0Y2023-06-08
45대구광역시남구봉덕1동스마트-6봉명네거리(현대블루핸즈 방향)대구광역시 남구 봉덕동 496-7대구광역시 남구 이천로75, (봉덕동)35.848119128.5982132019-06-18스마트형4.04.0Y2023-06-08
56대구광역시남구봉덕1동파라솔-1남구청네거리(kb국민은행앞)대구광역시 남구 봉덕동 606-11대구광역시 남구 이천로46, (봉덕동)35.845107128.5984892018-09-07고정형3.34.0Y2023-06-08
67대구광역시남구봉덕2동파라솔-18상동교교차로(래미안웰리스트 방향)대구광역시 남구 봉덕동 1617-18<NA>35.83506128.6051992018-08-24고정형3.34.0Y2023-06-08
78대구광역시남구봉덕3동스마트-3헬기장교차로(카젠정비소 방향)대구광역시 남구 봉덕동 626-8<NA>35.840926128.598272019-06-18스마트형3.05.0Y2023-06-08
89대구광역시남구봉덕3동파라솔-2남구청네거리(남경북약국 앞)대구광역시 남구 봉덕동 610-4대구광역시 남구 이천로40, (봉덕동)35.84454128.5984742018-08-24고정형3.34.0Y2023-06-08
910대구광역시남구봉덕3동파라솔-3남구청네거리(t월드대리점 앞)대구광역시 남구 봉덕동 632-1대구광역시 남구 이천로39, (봉덕동)35.844578128.5980092018-08-24고정형3.34.0Y2023-06-08
연번시도시군구읍면동(행정동)관리번호설치장소명지번주소도로명주소위도경도설치일자그늘막 유형전체높이(m)펼침지름(m)보험가입유무데이터기준일자
3233대구광역시남구봉덕3동스마트-11호텔더팔레스 앞대구광역시 남구 봉덕동 688-1대구광역시 남구 이천로13, (봉덕동)35.842309128.5975942020-07-03스마트형3.05.4Y2023-06-08
3334대구광역시남구대명1동파라솔-21앞산네거리(티브로드 방면)대구광역시 남구 대명동 784-6대구광역시 남구 대명로205, (대명동)35.840352128.579482020-06-30고정형3.33.8Y2023-06-08
3435대구광역시남구대명10동파라솔-22대구가톨릭병원교차로(전인병원 앞)대구광역시 남구 대명동 3056-35<NA>35.843842128.569622020-06-30고정형3.33.8Y2023-06-08
3536대구광역시남구이천동스마트-14건들바위 네거리(서봉사 방향)대구광역시 남구 이천동 437-2<NA>35.855583128.5979822021-09-16스마트형3.05.4Y2023-06-08
3637대구광역시남구이천동스마트-15희망교 교통섬(백년가약 방면)대구광역시 남구 이천동 569-12<NA>35.846811128.6056872021-09-16스마트형3.04.4Y2023-06-08
3738대구광역시남구봉덕2동스마트-16중동교 교통섬(영무예다음 방면)대구광역시 남구 봉덕동 883-10<NA>35.841283128.6061652021-09-16스마트형3.05.4Y2023-06-08
3839대구광역시남구봉덕2동스마트-17상동교 네거리대구광역시 남구 봉덕동 1203-6<NA>35.834474128.6051832021-09-16스마트형3.04.4Y2023-06-08
3940대구광역시남구봉덕2동스마트-18영무예다음 앞대구광역시 남구 봉덕동 929-2<NA>35.841022128.6044752021-09-16스마트형3.04.4Y2023-06-08
4041대구광역시남구대명9동스마트-19앞산네거리 교통섬대구광역시 남구 대명로 202<NA>35.8397128.5796022021-09-16스마트형3.04.4Y2023-06-08
4142대구광역시남구대명9동스마트-20미군부대(캠프워커교차로) 앞대구광역시 남구 대명동 286-4<NA>35.841414128.5828422021-09-16스마트형3.04.4Y2023-06-08