Overview

Dataset statistics

Number of variables10
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory85.8 B

Variable types

Text4
Categorical3
Numeric3

Dataset

Description대구광역시 동구_주유소 정보_20220822
Author대구광역시 동구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3057600&dataSetDetailId=30576001d2af9e8be887&provdMethod=FILE

Alerts

데이터기준일 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
행정동명 is highly overall correlated with 법정동명High correlation
법정동명 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
사업장명 has unique valuesUnique
소재지지번주소 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
전화번호 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-04-21 23:58:29.730305
Analysis finished2024-04-21 23:58:30.984707
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-22T08:58:31.142440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length7.8958333
Min length5

Characters and Unicode

Total characters379
Distinct characters115
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row(주)에스제이에너지금호강주유소
2nd row(주)한대동부주유소
3rd rowe-편한주유소
4th rowSK동호주유소
5th row계명Ⅱ주유소
ValueCountFrequency (%)
현대오일뱅크(주)직영 2
 
3.6%
주)에스제이에너지금호강주유소 1
 
1.8%
㈜미니에너지 1
 
1.8%
삼원주유소 1
 
1.8%
송정주유소 1
 
1.8%
신암로주유소 1
 
1.8%
안심주유소 1
 
1.8%
영진주유소 1
 
1.8%
우성주유소 1
 
1.8%
율하주유소 1
 
1.8%
Other values (44) 44
80.0%
2024-04-22T08:58:31.479998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 55
 
14.5%
유 49
 
12.9%
소 48
 
12.7%
대 16
 
4.2%
동 12
 
3.2%
( 7
 
1.8%
) 7
 
1.8%
7
 
1.8%
에 5
 
1.3%
구 5
 
1.3%
Other values (105) 168
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 338
89.2%
Open Punctuation 7
 
1.8%
Close Punctuation 7
 
1.8%
Space Separator 7
 
1.8%
Decimal Number 6
 
1.6%
Lowercase Letter 5
 
1.3%
Uppercase Letter 4
 
1.1%
Other Symbol 3
 
0.8%
Letter Number 1
 
0.3%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 55
16.3%
유 49
 
14.5%
소 48
 
14.2%
대 16
 
4.7%
동 12
 
3.6%
에 5
 
1.5%
구 5
 
1.5%
영 4
 
1.2%
성 4
 
1.2%
원 4
 
1.2%
Other values (86) 136
40.2%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 1
16.7%
5 1
16.7%
0 1
16.7%
8 1
16.7%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
s 1
20.0%
l 1
20.0%
f 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
25.0%
I 1
25.0%
K 1
25.0%
S 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 341
90.0%
Common 28
 
7.4%
Latin 10
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 55
 
16.1%
유 49
 
14.4%
소 48
 
14.1%
대 16
 
4.7%
동 12
 
3.5%
에 5
 
1.5%
구 5
 
1.5%
영 4
 
1.2%
성 4
 
1.2%
원 4
 
1.2%
Other values (87) 139
40.8%
Common
ValueCountFrequency (%)
( 7
25.0%
) 7
25.0%
7
25.0%
2 2
 
7.1%
- 1
 
3.6%
1 1
 
3.6%
5 1
 
3.6%
0 1
 
3.6%
8 1
 
3.6%
Latin
ValueCountFrequency (%)
e 2
20.0%
s 1
10.0%
l 1
10.0%
f 1
10.0%
C 1
10.0%
I 1
10.0%
Ⅱ 1
10.0%
K 1
10.0%
S 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 338
89.2%
ASCII 37
 
9.8%
None 3
 
0.8%
Number Forms 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 55
16.3%
유 49
 
14.5%
소 48
 
14.2%
대 16
 
4.7%
동 12
 
3.6%
에 5
 
1.5%
구 5
 
1.5%
영 4
 
1.2%
성 4
 
1.2%
원 4
 
1.2%
Other values (86) 136
40.2%
ASCII
ValueCountFrequency (%)
( 7
18.9%
) 7
18.9%
7
18.9%
2 2
 
5.4%
e 2
 
5.4%
s 1
 
2.7%
l 1
 
2.7%
f 1
 
2.7%
C 1
 
2.7%
I 1
 
2.7%
Other values (7) 7
18.9%
None
ValueCountFrequency (%)
㈜ 3
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지지번주소
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-22T08:58:31.687884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length25.270833
Min length19

Characters and Unicode

Total characters1213
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row대구광역시 동구 불로동 972-1번지
2nd row대구광역시 동구 용계동 916-1번지 외 2필지(916-4, 915-1)
3rd row대구광역시 동구 신암동 704-7.10.11.13번지
4th row대구광역시 동구 동호동 364번지
5th row대구광역시 동구 신서동 1138-1번지
ValueCountFrequency (%)
대구광역시 48
22.5%
동구 48
22.5%
용계동 7
 
3.3%
방촌동 4
 
1.9%
지저동 4
 
1.9%
지묘동 3
 
1.4%
봉무동 3
 
1.4%
괴전동 3
 
1.4%
효목동 3
 
1.4%
검사동 3
 
1.4%
Other values (81) 87
40.8%
2024-04-22T08:58:32.011341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
208
17.1%
동 99
 
8.2%
구 96
 
7.9%
1 70
 
5.8%
- 64
 
5.3%
지 56
 
4.6%
대 50
 
4.1%
광 48
 
4.0%
역 48
 
4.0%
시 48
 
4.0%
Other values (46) 426
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 584
48.1%
Decimal Number 325
26.8%
Space Separator 208
 
17.1%
Dash Punctuation 64
 
5.3%
Other Punctuation 28
 
2.3%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 99
17.0%
구 96
16.4%
지 56
9.6%
대 50
8.6%
광 48
8.2%
역 48
8.2%
시 48
8.2%
번 47
8.0%
용 8
 
1.4%
계 7
 
1.2%
Other values (30) 77
13.2%
Decimal Number
ValueCountFrequency (%)
1 70
21.5%
4 43
13.2%
6 38
11.7%
2 36
11.1%
3 29
8.9%
5 28
 
8.6%
7 22
 
6.8%
9 22
 
6.8%
8 19
 
5.8%
0 18
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 22
78.6%
. 6
 
21.4%
Space Separator
ValueCountFrequency (%)
208
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 629
51.9%
Hangul 584
48.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 99
17.0%
구 96
16.4%
지 56
9.6%
대 50
8.6%
광 48
8.2%
역 48
8.2%
시 48
8.2%
번 47
8.0%
용 8
 
1.4%
계 7
 
1.2%
Other values (30) 77
13.2%
Common
ValueCountFrequency (%)
208
33.1%
1 70
 
11.1%
- 64
 
10.2%
4 43
 
6.8%
6 38
 
6.0%
2 36
 
5.7%
3 29
 
4.6%
5 28
 
4.5%
7 22
 
3.5%
, 22
 
3.5%
Other values (6) 69
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 629
51.9%
Hangul 584
48.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
208
33.1%
1 70
 
11.1%
- 64
 
10.2%
4 43
 
6.8%
6 38
 
6.0%
2 36
 
5.7%
3 29
 
4.6%
5 28
 
4.5%
7 22
 
3.5%
, 22
 
3.5%
Other values (6) 69
 
11.0%
Hangul
ValueCountFrequency (%)
동 99
17.0%
구 96
16.4%
지 56
9.6%
대 50
8.6%
광 48
8.2%
역 48
8.2%
시 48
8.2%
번 47
8.0%
용 8
 
1.4%
계 7
 
1.2%
Other values (30) 77
13.2%

소재지도로명주소
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-22T08:58:32.272067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length22.0625
Min length21

Characters and Unicode

Total characters1059
Distinct characters71
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

Unique48 ?
Unique (%)100.0%

Sample

1st row대구광역시 동구 공항로 136 (불로동)
2nd row대구광역시 동구 동촌로 445 (용계동)
3rd row대구광역시 동구 경대로 54 (신암동)
4th row대구광역시 동구 안심로 346 (동호동)
5th row대구광역시 동구 이노밸리로 277 (신서동)
ValueCountFrequency (%)
대구광역시 48
20.4%
동구 48
20.4%
안심로 8
 
3.4%
동촌로 8
 
3.4%
용계동 7
 
3.0%
공항로 5
 
2.1%
팔공로 5
 
2.1%
화랑로 5
 
2.1%
지저동 4
 
1.7%
방촌동 4
 
1.7%
Other values (74) 93
39.6%
2024-04-22T08:58:32.632295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
187
17.7%
동 111
 
10.5%
구 96
 
9.1%
대 52
 
4.9%
로 50
 
4.7%
) 48
 
4.5%
광 48
 
4.5%
역 48
 
4.5%
시 48
 
4.5%
( 48
 
4.5%
Other values (61) 323
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 632
59.7%
Space Separator 187
 
17.7%
Decimal Number 142
 
13.4%
Close Punctuation 48
 
4.5%
Open Punctuation 48
 
4.5%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 111
17.6%
구 96
15.2%
대 52
 
8.2%
로 50
 
7.9%
광 48
 
7.6%
역 48
 
7.6%
시 48
 
7.6%
촌 12
 
1.9%
공 12
 
1.9%
계 10
 
1.6%
Other values (47) 145
22.9%
Decimal Number
ValueCountFrequency (%)
4 24
16.9%
1 21
14.8%
3 18
12.7%
2 15
10.6%
5 13
9.2%
8 13
9.2%
7 12
8.5%
0 11
7.7%
9 8
 
5.6%
6 7
 
4.9%
Space Separator
ValueCountFrequency (%)
187
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 632
59.7%
Common 427
40.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 111
17.6%
구 96
15.2%
대 52
 
8.2%
로 50
 
7.9%
광 48
 
7.6%
역 48
 
7.6%
시 48
 
7.6%
촌 12
 
1.9%
공 12
 
1.9%
계 10
 
1.6%
Other values (47) 145
22.9%
Common
ValueCountFrequency (%)
187
43.8%
) 48
 
11.2%
( 48
 
11.2%
4 24
 
5.6%
1 21
 
4.9%
3 18
 
4.2%
2 15
 
3.5%
5 13
 
3.0%
8 13
 
3.0%
7 12
 
2.8%
Other values (4) 28
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 632
59.7%
ASCII 427
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
187
43.8%
) 48
 
11.2%
( 48
 
11.2%
4 24
 
5.6%
1 21
 
4.9%
3 18
 
4.2%
2 15
 
3.5%
5 13
 
3.0%
8 13
 
3.0%
7 12
 
2.8%
Other values (4) 28
 
6.6%
Hangul
ValueCountFrequency (%)
동 111
17.6%
구 96
15.2%
대 52
 
8.2%
로 50
 
7.9%
광 48
 
7.6%
역 48
 
7.6%
시 48
 
7.6%
촌 12
 
1.9%
공 12
 
1.9%
계 10
 
1.6%
Other values (47) 145
22.9%

행정동명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Memory size516.0 B
안심2동
8 
공산동
6 
불로.봉무동
5 
지저동
4 
안심3동
4 
Other values (12)
21 

Length

Max length6
Median length5
Mean length3.8125
Min length3

Unique

Unique5 ?
Unique (%)10.4%

Sample

1st row불로.봉무동
2nd row안심2동
3rd row신암1동
4th row안심3동
5th row혁신동

Common Values

ValueCountFrequency (%)
안심2동 8
16.7%
공산동 6
12.5%
불로.봉무동 5
10.4%
지저동 4
8.3%
안심3동 4
8.3%
동촌동 3
 
6.2%
안심4동 3
 
6.2%
효목2동 2
 
4.2%
해안동 2
 
4.2%
효목1동 2
 
4.2%
Other values (7) 9
18.8%

Length

2024-04-22T08:58:32.761222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안심2동 8
16.7%
공산동 6
12.5%
불로.봉무동 5
10.4%
지저동 4
8.3%
안심3동 4
8.3%
동촌동 3
 
6.2%
안심4동 3
 
6.2%
혁신동 2
 
4.2%
방촌동 2
 
4.2%
해안동 2
 
4.2%
Other values (7) 9
18.8%

법정동명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size516.0 B
용계동
7 
방촌동
4 
효목동
4 
지저동
4 
검사동
3 
Other values (15)
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique7 ?
Unique (%)14.6%

Sample

1st row불로동
2nd row용계동
3rd row신암동
4th row동호동
5th row신서동

Common Values

ValueCountFrequency (%)
용계동 7
14.6%
방촌동 4
 
8.3%
효목동 4
 
8.3%
지저동 4
 
8.3%
검사동 3
 
6.2%
봉무동 3
 
6.2%
지묘동 3
 
6.2%
괴전동 3
 
6.2%
신천동 2
 
4.2%
신서동 2
 
4.2%
Other values (10) 13
27.1%

Length

2024-04-22T08:58:32.864122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용계동 7
14.6%
방촌동 4
 
8.3%
효목동 4
 
8.3%
지저동 4
 
8.3%
검사동 3
 
6.2%
봉무동 3
 
6.2%
지묘동 3
 
6.2%
괴전동 3
 
6.2%
불로동 2
 
4.2%
신암동 2
 
4.2%
Other values (10) 13
27.1%

소재지면적
Real number (ℝ)

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1149.9167
Minimum316
Maximum3766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-22T08:58:32.964370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum316
5-th percentile450.35
Q1665.75
median1002.5
Q31457
95-th percentile2077.35
Maximum3766
Range3450
Interquartile range (IQR)791.25

Descriptive statistics

Standard deviation635.88865
Coefficient of variation (CV)0.55298672
Kurtosis5.2347125
Mean1149.9167
Median Absolute Deviation (MAD)339.5
Skewness1.8548255
Sum55196
Variance404354.38
MonotonicityNot monotonic
2024-04-22T08:58:33.080114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
665 2
 
4.2%
1677 1
 
2.1%
785 1
 
2.1%
954 1
 
2.1%
1003 1
 
2.1%
870 1
 
2.1%
1487 1
 
2.1%
1447 1
 
2.1%
865 1
 
2.1%
2713 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
316 1
2.1%
407 1
2.1%
450 1
2.1%
451 1
2.1%
525 1
2.1%
593 1
2.1%
635 1
2.1%
640 1
2.1%
662 1
2.1%
664 1
2.1%
ValueCountFrequency (%)
3766 1
2.1%
2713 1
2.1%
2112 1
2.1%
2013 1
2.1%
1869 1
2.1%
1814 1
2.1%
1722 1
2.1%
1677 1
2.1%
1670 1
2.1%
1643 1
2.1%

전화번호
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-22T08:58:33.282596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters576
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row053-984-5500
2nd row053-964-2211
3rd row053-954-6637
4th row053-943-0077
5th row053-965-5152
ValueCountFrequency (%)
053-984-5500 1
 
2.1%
053-964-2211 1
 
2.1%
053-559-5151 1
 
2.1%
053-985-5599 1
 
2.1%
053-981-8484 1
 
2.1%
053-982-5154 1
 
2.1%
053-965-3336 1
 
2.1%
053-953-5144 1
 
2.1%
053-962-5052 1
 
2.1%
053-984-8288 1
 
2.1%
Other values (38) 38
79.2%
2024-04-22T08:58:33.611018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 103
17.9%
- 96
16.7%
0 80
13.9%
3 67
11.6%
9 57
9.9%
8 39
 
6.8%
1 33
 
5.7%
2 32
 
5.6%
6 27
 
4.7%
4 25
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 480
83.3%
Dash Punctuation 96
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 103
21.5%
0 80
16.7%
3 67
14.0%
9 57
11.9%
8 39
 
8.1%
1 33
 
6.9%
2 32
 
6.7%
6 27
 
5.6%
4 25
 
5.2%
7 17
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 103
17.9%
- 96
16.7%
0 80
13.9%
3 67
11.6%
9 57
9.9%
8 39
 
6.8%
1 33
 
5.7%
2 32
 
5.6%
6 27
 
4.7%
4 25
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 103
17.9%
- 96
16.7%
0 80
13.9%
3 67
11.6%
9 57
9.9%
8 39
 
6.8%
1 33
 
5.7%
2 32
 
5.6%
6 27
 
4.7%
4 25
 
4.3%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.893835
Minimum35.865771
Maximum35.991435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-22T08:58:33.740303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.865771
5-th percentile35.86947
Q135.873126
median35.879435
Q335.896645
95-th percentile35.965522
Maximum35.991435
Range0.12566456
Interquartile range (IQR)0.02351884

Descriptive statistics

Standard deviation0.032124497
Coefficient of variation (CV)0.00089498647
Kurtosis2.3639245
Mean35.893835
Median Absolute Deviation (MAD)0.007142275
Skewness1.7902935
Sum1722.9041
Variance0.0010319833
MonotonicityNot monotonic
2024-04-22T08:58:33.863668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
35.90027939 1
 
2.1%
35.8723032 1
 
2.1%
35.89625931 1
 
2.1%
35.8971326 1
 
2.1%
35.87231699 1
 
2.1%
35.88107312 1
 
2.1%
35.87174743 1
 
2.1%
35.88721314 1
 
2.1%
35.86577059 1
 
2.1%
35.8730224 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
35.86577059 1
2.1%
35.86714695 1
2.1%
35.86907966 1
2.1%
35.87019397 1
2.1%
35.87174743 1
2.1%
35.87176235 1
2.1%
35.87228309 1
2.1%
35.8723032 1
2.1%
35.87231699 1
2.1%
35.87262147 1
2.1%
ValueCountFrequency (%)
35.99143515 1
2.1%
35.9879081 1
2.1%
35.96814745 1
2.1%
35.96064675 1
2.1%
35.95305388 1
2.1%
35.94023239 1
2.1%
35.93240989 1
2.1%
35.92830385 1
2.1%
35.91603598 1
2.1%
35.91313666 1
2.1%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.6671
Minimum128.61218
Maximum128.74732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-22T08:58:34.286432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.61218
5-th percentile128.618
Q1128.64012
median128.65456
Q3128.69149
95-th percentile128.73653
Maximum128.74732
Range0.1351398
Interquartile range (IQR)0.05136545

Descriptive statistics

Standard deviation0.037877233
Coefficient of variation (CV)0.00029438165
Kurtosis-0.77964827
Mean128.6671
Median Absolute Deviation (MAD)0.02315825
Skewness0.60832994
Sum6176.0207
Variance0.0014346848
MonotonicityNot monotonic
2024-04-22T08:58:34.422453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
128.6297351 1
 
2.1%
128.710242 1
 
2.1%
128.6399829 1
 
2.1%
128.6401672 1
 
2.1%
128.7363433 1
 
2.1%
128.6121836 1
 
2.1%
128.7366259 1
 
2.1%
128.65113 1
 
2.1%
128.7114349 1
 
2.1%
128.6998279 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
128.6121836 1
2.1%
128.6156549 1
2.1%
128.6163976 1
2.1%
128.6209855 1
2.1%
128.6289893 1
2.1%
128.6297351 1
2.1%
128.6298646 1
2.1%
128.6319224 1
2.1%
128.6343559 1
2.1%
128.6346741 1
2.1%
ValueCountFrequency (%)
128.7473234 1
2.1%
128.738129 1
2.1%
128.7366259 1
2.1%
128.7363433 1
2.1%
128.7297791 1
2.1%
128.7272869 1
2.1%
128.7224318 1
2.1%
128.7114349 1
2.1%
128.710242 1
2.1%
128.7084141 1
2.1%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
2022-08-22
48 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-22
2nd row2022-08-22
3rd row2022-08-22
4th row2022-08-22
5th row2022-08-22

Common Values

ValueCountFrequency (%)
2022-08-22 48
100.0%

Length

2024-04-22T08:58:34.537312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T08:58:34.611304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-22 48
100.0%

Interactions

2024-04-22T08:58:30.556656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T08:58:30.088158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T08:58:30.327418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T08:58:30.634117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T08:58:30.161655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T08:58:30.402493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T08:58:30.709649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T08:58:30.252079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T08:58:30.480267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T08:58:34.665674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장명소재지지번주소소재지도로명주소행정동명법정동명소재지면적전화번호위도경도
사업장명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동명1.0001.0001.0001.0000.9570.7711.0000.4400.845
법정동명1.0001.0001.0000.9571.0000.6931.0000.9200.967
소재지면적1.0001.0001.0000.7710.6931.0001.0000.0000.615
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.4400.9200.0001.0001.0000.000
경도1.0001.0001.0000.8450.9670.6151.0000.0001.000
2024-04-22T08:58:34.765110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명법정동명
행정동명1.0000.676
법정동명0.6761.000
2024-04-22T08:58:34.837994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지면적위도경도행정동명법정동명
소재지면적1.000-0.4300.3230.3220.280
위도-0.4301.000-0.6250.1420.486
경도0.323-0.6251.0000.4780.612
행정동명0.3220.1420.4781.0000.676
법정동명0.2800.4860.6120.6761.000

Missing values

2024-04-22T08:58:30.815269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T08:58:30.936722image/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

사업장명소재지지번주소소재지도로명주소행정동명법정동명소재지면적전화번호위도경도데이터기준일
0(주)에스제이에너지금호강주유소대구광역시 동구 불로동 972-1번지대구광역시 동구 공항로 136 (불로동)불로.봉무동불로동1677053-984-550035.900279128.6297352022-08-22
1(주)한대동부주유소대구광역시 동구 용계동 916-1번지 외 2필지(916-4, 915-1)대구광역시 동구 동촌로 445 (용계동)안심2동용계동3766053-964-221135.874879128.6873082022-08-22
2e-편한주유소대구광역시 동구 신암동 704-7.10.11.13번지대구광역시 동구 경대로 54 (신암동)신암1동신암동316053-954-663735.885968128.6156552022-08-22
3SK동호주유소대구광역시 동구 동호동 364번지대구광역시 동구 안심로 346 (동호동)안심3동동호동1088053-943-007735.867147128.7224322022-08-22
4계명Ⅱ주유소대구광역시 동구 신서동 1138-1번지대구광역시 동구 이노밸리로 277 (신서동)혁신동신서동1167053-965-515235.878582128.7272872022-08-22
5국가대표제2주유소대구광역시 동구 방촌동 1084-700번지대구광역시 동구 화랑로 377 (방촌동)방촌동방촌동1002053-986-510035.87762128.6648952022-08-22
6국제주유소대구광역시 동구 봉무동 143-2번지대구광역시 동구 팔공로 376 (봉무동)불로.봉무동봉무동1161053-986-515235.93241128.6435552022-08-22
7극동주유소대구광역시 동구 신천동 283-2,19번지대구광역시 동구 동부로 80 (신천동)신천3동신천동2112053-752-222035.875034128.6209852022-08-22
8글로벌제2주유소대구광역시 동구 지묘동 275번지대구광역시 동구 팔공로 473 (지묘동)공산동지묘동907053-986-510435.940232128.6453922022-08-22
9대성산업(주)강촌대성주유소대구광역시 동구 용계동 437-56번지 외 1필지(437-45)대구광역시 동구 화랑로 481 (용계동)안심2동용계동1869053-985-422235.875511128.6760842022-08-22
사업장명소재지지번주소소재지도로명주소행정동명법정동명소재지면적전화번호위도경도데이터기준일
38㈜미니에너지 직영주유소대구광역시 동구 용계동 447-4번지 447-6,448-4.대구광역시 동구 화랑로 484 (용계동)안심2동용계동999053-963-005135.874886128.6767212022-08-22
39주식회사 에이치씨대하 해안주유소대구광역시 동구 불로동 1158-14번지대구광역시 동구 팔공로 157 (불로동)불로.봉무동불로동1017053-985-600135.913137128.6407842022-08-22
40지성주유소대구광역시 동구 검사동 957-9번지대구광역시 동구 동촌로 155(검사동)동촌동검사동771053-985-770835.884328128.6579842022-08-22
41지에스칼텍스㈜ 대구혁신도시주유소대구광역시 동구 율암동 1131번지대구광역시 동구 혁신대로 104(율암동)안심2동율암동1722053-963-916035.878425128.7084142022-08-22
42팔공김치주유소대구광역시 동구 방촌동 857-60,61번지대구광역시 동구 동촌로 291-4 (방촌동)해안동방촌동1643053-983-871335.879582128.6717892022-08-22
43팔공산(IC)주유소대구광역시 동구 지저동 662-17번지 662-44, 684-14, 684-16대구광역시 동구 공항로 247 (지저동)지저동지저동450053-982-093435.896482128.6404722022-08-22
44현대오일뱅크(주)직영 대구공항셀프주유소대구광역시 동구 지저동 678-6번지대구광역시 동구 공항로 259 (지저동)지저동지저동923053-984-655535.895893128.6410772022-08-22
45현대오일뱅크(주)직영 동진주유소대구광역시 동구 용계동 1028-1번지대구광역시 동구 동촌로 355 (용계동)안심2동용계동2013053-984-770035.877536128.6782392022-08-22
46효동로주유소대구광역시 동구 효목동 137-49번지대구광역시 동구 효동로 77 (효목동)효목1동효목동635053-944-456735.883351128.6435562022-08-22
47효목주유소대구광역시 동구 효목동 289-2, -7번지대구광역시 동구 동부로 211 (효목동)효목2동효목동1028053-745-390035.879289128.6346742022-08-22