Overview

Dataset statistics

Number of variables3
Number of observations42
Missing cells21
Missing cells (%)16.7%
Duplicate rows1
Duplicate rows (%)2.4%
Total size in memory1.2 KiB
Average record size in memory28.1 B

Variable types

Numeric1
Text2

Dataset

Description부산광역시남구_석유판매업현황_20190731
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15055820

Alerts

Dataset has 1 (2.4%) duplicate rowsDuplicates
순번 has 7 (16.7%) missing valuesMissing
주유소명 has 7 (16.7%) missing valuesMissing
주 소(소재지) has 7 (16.7%) missing valuesMissing

Reproduction

Analysis started2023-12-10 16:26:42.017201
Analysis finished2023-12-10 16:26:42.508451
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

MISSING 

Distinct35
Distinct (%)100.0%
Missing7
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean18
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T01:26:42.586526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7
Q19.5
median18
Q326.5
95-th percentile33.3
Maximum35
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.246951
Coefficient of variation (CV)0.56927504
Kurtosis-1.2
Mean18
Median Absolute Deviation (MAD)9
Skewness0
Sum630
Variance105
MonotonicityStrictly increasing
2023-12-11T01:26:42.763617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 1
 
2.4%
21 1
 
2.4%
22 1
 
2.4%
23 1
 
2.4%
24 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
Other values (25) 25
59.5%
(Missing) 7
 
16.7%
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 (%)
35 1
2.4%
34 1
2.4%
33 1
2.4%
32 1
2.4%
31 1
2.4%
30 1
2.4%
29 1
2.4%
28 1
2.4%
27 1
2.4%
26 1
2.4%

주유소명
Text

MISSING 

Distinct35
Distinct (%)100.0%
Missing7
Missing (%)16.7%
Memory size468.0 B
2023-12-11T01:26:43.038312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length10.485714
Min length5

Characters and Unicode

Total characters367
Distinct characters92
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row굿오일주유소
2nd row(주)좋은기름나라 북항대교주유소
3rd row내트럭(주)부산용당사업소
4th row내트럭(주)부산사업소
5th row(주)OS에너지 신선대주유소
ValueCountFrequency (%)
sk네트웍스(주 2
 
4.4%
주)os에너지 2
 
4.4%
주식회사 2
 
4.4%
주)좋은기름나라 1
 
2.2%
박물관주유소 1
 
2.2%
반도주유소 1
 
2.2%
주)경인석유 1
 
2.2%
구도일주유소용호 1
 
2.2%
대성주유소 1
 
2.2%
분포셀프주유소 1
 
2.2%
Other values (32) 32
71.1%
2023-12-11T01:26:43.459492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 51
 
13.9%
유 36
 
9.8%
소 31
 
8.4%
( 21
 
5.7%
) 21
 
5.7%
지 11
 
3.0%
스 11
 
3.0%
10
 
2.7%
에 9
 
2.5%
대 8
 
2.2%
Other values (82) 158
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 307
83.7%
Open Punctuation 21
 
5.7%
Close Punctuation 21
 
5.7%
Space Separator 10
 
2.7%
Uppercase Letter 8
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 51
 
16.6%
유 36
 
11.7%
소 31
 
10.1%
지 11
 
3.6%
스 11
 
3.6%
에 9
 
2.9%
대 8
 
2.6%
성 5
 
1.6%
너 5
 
1.6%
용 5
 
1.6%
Other values (76) 135
44.0%
Uppercase Letter
ValueCountFrequency (%)
S 4
50.0%
K 2
25.0%
O 2
25.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 307
83.7%
Common 52
 
14.2%
Latin 8
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 51
 
16.6%
유 36
 
11.7%
소 31
 
10.1%
지 11
 
3.6%
스 11
 
3.6%
에 9
 
2.9%
대 8
 
2.6%
성 5
 
1.6%
너 5
 
1.6%
용 5
 
1.6%
Other values (76) 135
44.0%
Common
ValueCountFrequency (%)
( 21
40.4%
) 21
40.4%
10
19.2%
Latin
ValueCountFrequency (%)
S 4
50.0%
K 2
25.0%
O 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 307
83.7%
ASCII 60
 
16.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 51
 
16.6%
유 36
 
11.7%
소 31
 
10.1%
지 11
 
3.6%
스 11
 
3.6%
에 9
 
2.9%
대 8
 
2.6%
성 5
 
1.6%
너 5
 
1.6%
용 5
 
1.6%
Other values (76) 135
44.0%
ASCII
ValueCountFrequency (%)
( 21
35.0%
) 21
35.0%
10
16.7%
S 4
 
6.7%
K 2
 
3.3%
O 2
 
3.3%

주 소(소재지)
Text

MISSING 

Distinct35
Distinct (%)100.0%
Missing7
Missing (%)16.7%
Memory size468.0 B
2023-12-11T01:26:43.767552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length22
Mean length23.8
Min length21

Characters and Unicode

Total characters833
Distinct characters53
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

Unique35 ?
Unique (%)100.0%

Sample

1st row부산광역시 남구 북항로 108 (감만동)
2nd row부산광역시 남구 신선로 316 (용당동)
3rd row부산광역시 남구 신선로 261 (용당동)
4th row부산광역시 남구 신선로 168 (감만동)
5th row부산광역시 남구 신선로 308 (용당동)
ValueCountFrequency (%)
부산광역시 35
19.6%
남구 35
19.6%
신선로 10
 
5.6%
대연동 10
 
5.6%
용당동 9
 
5.0%
유엔로 5
 
2.8%
문현동 4
 
2.2%
우암로 4
 
2.2%
우암동 3
 
1.7%
용호로 3
 
1.7%
Other values (53) 61
34.1%
2023-12-11T01:26:44.302881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
146
 
17.5%
( 38
 
4.6%
) 38
 
4.6%
부 35
 
4.2%
광 35
 
4.2%
역 35
 
4.2%
시 35
 
4.2%
남 35
 
4.2%
구 35
 
4.2%
산 35
 
4.2%
Other values (43) 366
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 473
56.8%
Space Separator 146
 
17.5%
Decimal Number 127
 
15.2%
Open Punctuation 38
 
4.6%
Close Punctuation 38
 
4.6%
Dash Punctuation 6
 
0.7%
Other Punctuation 5
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 35
 
7.4%
광 35
 
7.4%
역 35
 
7.4%
시 35
 
7.4%
남 35
 
7.4%
구 35
 
7.4%
산 35
 
7.4%
동 35
 
7.4%
로 34
 
7.2%
대 16
 
3.4%
Other values (28) 143
30.2%
Decimal Number
ValueCountFrequency (%)
2 23
18.1%
1 23
18.1%
3 15
11.8%
4 15
11.8%
7 10
7.9%
8 9
 
7.1%
9 8
 
6.3%
6 8
 
6.3%
0 8
 
6.3%
5 8
 
6.3%
Space Separator
ValueCountFrequency (%)
146
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 473
56.8%
Common 360
43.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 35
 
7.4%
광 35
 
7.4%
역 35
 
7.4%
시 35
 
7.4%
남 35
 
7.4%
구 35
 
7.4%
산 35
 
7.4%
동 35
 
7.4%
로 34
 
7.2%
대 16
 
3.4%
Other values (28) 143
30.2%
Common
ValueCountFrequency (%)
146
40.6%
( 38
 
10.6%
) 38
 
10.6%
2 23
 
6.4%
1 23
 
6.4%
3 15
 
4.2%
4 15
 
4.2%
7 10
 
2.8%
8 9
 
2.5%
9 8
 
2.2%
Other values (5) 35
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 473
56.8%
ASCII 360
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
146
40.6%
( 38
 
10.6%
) 38
 
10.6%
2 23
 
6.4%
1 23
 
6.4%
3 15
 
4.2%
4 15
 
4.2%
7 10
 
2.8%
8 9
 
2.5%
9 8
 
2.2%
Other values (5) 35
 
9.7%
Hangul
ValueCountFrequency (%)
부 35
 
7.4%
광 35
 
7.4%
역 35
 
7.4%
시 35
 
7.4%
남 35
 
7.4%
구 35
 
7.4%
산 35
 
7.4%
동 35
 
7.4%
로 34
 
7.2%
대 16
 
3.4%
Other values (28) 143
30.2%

Interactions

2023-12-11T01:26:42.163412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:26:44.432529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번주유소명주 소(소재지)
순번1.0001.0001.000
주유소명1.0001.0001.000
주 소(소재지)1.0001.0001.000

Missing values

2023-12-11T01:26:42.269226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:26:42.348112image/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.
2023-12-11T01:26:42.442411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

순번주유소명주 소(소재지)
01굿오일주유소부산광역시 남구 북항로 108 (감만동)
12(주)좋은기름나라 북항대교주유소부산광역시 남구 신선로 316 (용당동)
23내트럭(주)부산용당사업소부산광역시 남구 신선로 261 (용당동)
34내트럭(주)부산사업소부산광역시 남구 신선로 168 (감만동)
45(주)OS에너지 신선대주유소부산광역시 남구 신선로 308 (용당동)
56금융단지주유소부산광역시 남구 전포대로 75 (문현동)
67(주)호림에너지(유정주유소)부산광역시 남구 수영로132번길 47 (대연동)
78주식회사 대호석유 용당씨와이주유소부산광역시 남구 신선로 392 (용당동)
89현대오일뱅크(주)직영용당현대주유소부산광역시 남구 신선로 252 (용당동)
910SK네트웍스(주) 동해주유소부산광역시 남구 수영로 179 (대연동)
순번주유소명주 소(소재지)
3233동명주유소부산광역시 남구 전포대로 7 (문현동)
3334(주)신선에너지 유엔주유소부산광역시 남구 석포로 134 (대연동,외 2필지(949-12,949-18))
3435매일주유소부산광역시 남구 수영로 22 (문현동)
35<NA><NA><NA>
36<NA><NA><NA>
37<NA><NA><NA>
38<NA><NA><NA>
39<NA><NA><NA>
40<NA><NA><NA>
41<NA><NA><NA>

Duplicate rows

Most frequently occurring

순번주유소명주 소(소재지)# duplicates
0<NA><NA><NA>7