Overview

Dataset statistics

Number of variables6
Number of observations21
Missing cells10
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory54.3 B

Variable types

Text4
Categorical2

Dataset

Description경상남도 사천시 관내에 경질유, 중질유 사용업체에 관한 데이터 입니다.(상호명, 주소, 사용연료, 생산품, 연간사용량)
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15107850

Alerts

데이터기준일자 has constant value ""Constant
생산품 has 7 (33.3%) missing valuesMissing
연간 사용량 has 3 (14.3%) missing valuesMissing
상호명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:20:33.063729
Analysis finished2023-12-11 00:20:33.563329
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상호명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T09:20:33.685146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length8.1428571
Min length4

Characters and Unicode

Total characters171
Distinct characters96
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row평화종합정비
2nd row하이종합정비
3rd row삼천포종합정비(주)
4th row베스트종합정비
5th row사천시 자원회수센터
ValueCountFrequency (%)
평화종합정비 1
 
4.2%
하이종합정비 1
 
4.2%
삼육비철 1
 
4.2%
㈜굿웰바이오 1
 
4.2%
주)세명공업 1
 
4.2%
주식회사 1
 
4.2%
제일 1
 
4.2%
농업회사법인 1
 
4.2%
주)태강 1
 
4.2%
사천시농협연합미곡종합처리장 1
 
4.2%
Other values (14) 14
58.3%
2023-12-11T09:20:34.023693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
합 9
 
5.3%
종 8
 
4.7%
비 8
 
4.7%
정 6
 
3.5%
사 6
 
3.5%
주 5
 
2.9%
스 5
 
2.9%
( 4
 
2.3%
천 4
 
2.3%
) 4
 
2.3%
Other values (86) 112
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 155
90.6%
Open Punctuation 4
 
2.3%
Close Punctuation 4
 
2.3%
Space Separator 3
 
1.8%
Other Symbol 3
 
1.8%
Decimal Number 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
합 9
 
5.8%
종 8
 
5.2%
비 8
 
5.2%
정 6
 
3.9%
사 6
 
3.9%
주 5
 
3.2%
스 5
 
3.2%
천 4
 
2.6%
공 3
 
1.9%
농 3
 
1.9%
Other values (80) 98
63.2%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
1 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 158
92.4%
Common 13
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
합 9
 
5.7%
종 8
 
5.1%
비 8
 
5.1%
정 6
 
3.8%
사 6
 
3.8%
주 5
 
3.2%
스 5
 
3.2%
천 4
 
2.5%
공 3
 
1.9%
농 3
 
1.9%
Other values (81) 101
63.9%
Common
ValueCountFrequency (%)
( 4
30.8%
) 4
30.8%
3
23.1%
3 1
 
7.7%
1 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 155
90.6%
ASCII 13
 
7.6%
None 3
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
합 9
 
5.8%
종 8
 
5.2%
비 8
 
5.2%
정 6
 
3.9%
사 6
 
3.9%
주 5
 
3.2%
스 5
 
3.2%
천 4
 
2.6%
공 3
 
1.9%
농 3
 
1.9%
Other values (80) 98
63.2%
ASCII
ValueCountFrequency (%)
( 4
30.8%
) 4
30.8%
3
23.1%
3 1
 
7.7%
1 1
 
7.7%
None
ValueCountFrequency (%)
㈜ 3
100.0%

주소
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T09:20:34.226022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length22.904762
Min length19

Characters and Unicode

Total characters481
Distinct characters72
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 (%)100.0%

Sample

1st row경상남도 사천시 곤양면 구고속도로 1636
2nd row경상남도 사천시 남일로 304 (향촌동)
3rd row경상남도 사천시 삼천포대교로 577 (좌룡동)
4th row경상남도 사천시 정동면 진삼로 1206
5th row경상남도 사천시 환경길 71 (사등동)
ValueCountFrequency (%)
경상남도 21
19.4%
사천시 21
19.4%
사남면 3
 
2.8%
축동면 3
 
2.8%
곤명면 3
 
2.8%
사천읍 3
 
2.8%
가산리 2
 
1.9%
정동면 2
 
1.9%
진삼로 2
 
1.9%
경서대로 2
 
1.9%
Other values (44) 46
42.6%
2023-12-11T09:20:34.742969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
18.5%
사 29
 
6.0%
천 27
 
5.6%
남 25
 
5.2%
경 24
 
5.0%
도 22
 
4.6%
상 21
 
4.4%
시 21
 
4.4%
2 15
 
3.1%
1 14
 
2.9%
Other values (62) 194
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 301
62.6%
Space Separator 89
 
18.5%
Decimal Number 75
 
15.6%
Dash Punctuation 6
 
1.2%
Close Punctuation 5
 
1.0%
Open Punctuation 5
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 29
 
9.6%
천 27
 
9.0%
남 25
 
8.3%
경 24
 
8.0%
도 22
 
7.3%
상 21
 
7.0%
시 21
 
7.0%
면 14
 
4.7%
로 13
 
4.3%
동 10
 
3.3%
Other values (48) 95
31.6%
Decimal Number
ValueCountFrequency (%)
2 15
20.0%
1 14
18.7%
4 11
14.7%
5 7
9.3%
6 7
9.3%
7 5
 
6.7%
8 5
 
6.7%
3 5
 
6.7%
0 4
 
5.3%
9 2
 
2.7%
Space Separator
ValueCountFrequency (%)
89
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 301
62.6%
Common 180
37.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 29
 
9.6%
천 27
 
9.0%
남 25
 
8.3%
경 24
 
8.0%
도 22
 
7.3%
상 21
 
7.0%
시 21
 
7.0%
면 14
 
4.7%
로 13
 
4.3%
동 10
 
3.3%
Other values (48) 95
31.6%
Common
ValueCountFrequency (%)
89
49.4%
2 15
 
8.3%
1 14
 
7.8%
4 11
 
6.1%
5 7
 
3.9%
6 7
 
3.9%
- 6
 
3.3%
7 5
 
2.8%
8 5
 
2.8%
) 5
 
2.8%
Other values (4) 16
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 301
62.6%
ASCII 180
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
49.4%
2 15
 
8.3%
1 14
 
7.8%
4 11
 
6.1%
5 7
 
3.9%
6 7
 
3.9%
- 6
 
3.3%
7 5
 
2.8%
8 5
 
2.8%
) 5
 
2.8%
Other values (4) 16
 
8.9%
Hangul
ValueCountFrequency (%)
사 29
 
9.6%
천 27
 
9.0%
남 25
 
8.3%
경 24
 
8.0%
도 22
 
7.3%
상 21
 
7.0%
시 21
 
7.0%
면 14
 
4.7%
로 13
 
4.3%
동 10
 
3.3%
Other values (48) 95
31.6%

사용연료
Categorical

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
경유
13 
등유
4 
중유C
2 
경유, 중유C
 
1
부생연료유1호
 
1

Length

Max length7
Median length2
Mean length2.5714286
Min length2

Unique

Unique2 ?
Unique (%)9.5%

Sample

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

Common Values

ValueCountFrequency (%)
경유 13
61.9%
등유 4
 
19.0%
중유C 2
 
9.5%
경유, 중유C 1
 
4.8%
부생연료유1호 1
 
4.8%

Length

2023-12-11T09:20:34.853655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:34.941790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경유 14
63.6%
등유 4
 
18.2%
중유c 3
 
13.6%
부생연료유1호 1
 
4.5%

생산품
Text

MISSING 

Distinct13
Distinct (%)92.9%
Missing7
Missing (%)33.3%
Memory size300.0 B
2023-12-11T09:20:35.076207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length8.5
Mean length6.7142857
Min length1

Characters and Unicode

Total characters94
Distinct characters51
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

Unique12 ?
Unique (%)85.7%

Sample

1st row정비자동차
2nd row항공기, 항공기부품, 건설기계
3rd row도장완료된 자동차
4th row수리된 자동차
5th row도장완료된 자동차
ValueCountFrequency (%)
자동차 3
 
13.0%
도장완료된 2
 
8.7%
항공기부품 2
 
8.7%
분말활성탄 1
 
4.3%
목재펠릿 1
 
4.3%
정제유 1
 
4.3%
재생수지칩 1
 
4.3%
온수 1
 
4.3%
및 1
 
4.3%
스팀 1
 
4.3%
Other values (9) 9
39.1%
2023-12-11T09:20:35.338706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
 
9.6%
기 5
 
5.3%
자 4
 
4.3%
동 4
 
4.3%
차 4
 
4.3%
품 3
 
3.2%
된 3
 
3.2%
수 3
 
3.2%
항 3
 
3.2%
공 3
 
3.2%
Other values (41) 53
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
87.2%
Space Separator 9
 
9.6%
Other Punctuation 3
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 5
 
6.1%
자 4
 
4.9%
동 4
 
4.9%
차 4
 
4.9%
품 3
 
3.7%
된 3
 
3.7%
수 3
 
3.7%
항 3
 
3.7%
공 3
 
3.7%
부 3
 
3.7%
Other values (39) 47
57.3%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82
87.2%
Common 12
 
12.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 5
 
6.1%
자 4
 
4.9%
동 4
 
4.9%
차 4
 
4.9%
품 3
 
3.7%
된 3
 
3.7%
수 3
 
3.7%
항 3
 
3.7%
공 3
 
3.7%
부 3
 
3.7%
Other values (39) 47
57.3%
Common
ValueCountFrequency (%)
9
75.0%
, 3
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82
87.2%
ASCII 12
 
12.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9
75.0%
, 3
 
25.0%
Hangul
ValueCountFrequency (%)
기 5
 
6.1%
자 4
 
4.9%
동 4
 
4.9%
차 4
 
4.9%
품 3
 
3.7%
된 3
 
3.7%
수 3
 
3.7%
항 3
 
3.7%
공 3
 
3.7%
부 3
 
3.7%
Other values (39) 47
57.3%

연간 사용량
Text

MISSING 

Distinct16
Distinct (%)88.9%
Missing3
Missing (%)14.3%
Memory size300.0 B
2023-12-11T09:20:35.469987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.6111111
Min length2

Characters and Unicode

Total characters101
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)83.3%

Sample

1st row2400L
2nd row39120L
3rd row56680L
4th row6000L
5th row9648L
ValueCountFrequency (%)
6000l 3
16.7%
2400l 1
 
5.6%
56680l 1
 
5.6%
9648l 1
 
5.6%
26640l 1
 
5.6%
17760l 1
 
5.6%
791138l 1
 
5.6%
39120l 1
 
5.6%
1200l 1
 
5.6%
540000l 1
 
5.6%
Other values (6) 6
33.3%
2023-12-11T09:20:35.699890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32
31.7%
L 17
16.8%
6 11
 
10.9%
4 7
 
6.9%
2 6
 
5.9%
9 6
 
5.9%
1 5
 
5.0%
5 4
 
4.0%
8 4
 
4.0%
7 4
 
4.0%
Other values (3) 5
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82
81.2%
Uppercase Letter 17
 
16.8%
Lowercase Letter 2
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32
39.0%
6 11
 
13.4%
4 7
 
8.5%
2 6
 
7.3%
9 6
 
7.3%
1 5
 
6.1%
5 4
 
4.9%
8 4
 
4.9%
7 4
 
4.9%
3 3
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
g 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
L 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82
81.2%
Latin 19
 
18.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32
39.0%
6 11
 
13.4%
4 7
 
8.5%
2 6
 
7.3%
9 6
 
7.3%
1 5
 
6.1%
5 4
 
4.9%
8 4
 
4.9%
7 4
 
4.9%
3 3
 
3.7%
Latin
ValueCountFrequency (%)
L 17
89.5%
k 1
 
5.3%
g 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32
31.7%
L 17
16.8%
6 11
 
10.9%
4 7
 
6.9%
2 6
 
5.9%
9 6
 
5.9%
1 5
 
5.0%
5 4
 
4.0%
8 4
 
4.0%
7 4
 
4.0%
Other values (3) 5
 
5.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-10-30
21 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-30
2nd row2023-10-30
3rd row2023-10-30
4th row2023-10-30
5th row2023-10-30

Common Values

ValueCountFrequency (%)
2023-10-30 21
100.0%

Length

2023-12-11T09:20:35.814446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:35.894361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-30 21
100.0%

Correlations

2023-12-11T09:20:35.948176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호명주소사용연료생산품연간 사용량
상호명1.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.000
사용연료1.0001.0001.0001.0001.000
생산품1.0001.0001.0001.0000.849
연간 사용량1.0001.0001.0000.8491.000

Missing values

2023-12-11T09:20:33.338616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:20:33.449206image/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.
2023-12-11T09:20:33.524546image/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

상호명주소사용연료생산품연간 사용량데이터기준일자
0평화종합정비경상남도 사천시 곤양면 구고속도로 1636경유<NA>2400L2023-10-30
1하이종합정비경상남도 사천시 남일로 304 (향촌동)경유<NA>39120L2023-10-30
2삼천포종합정비(주)경상남도 사천시 삼천포대교로 577 (좌룡동)경유<NA>56680L2023-10-30
3베스트종합정비경상남도 사천시 정동면 진삼로 1206경유정비자동차6000L2023-10-30
4사천시 자원회수센터경상남도 사천시 환경길 71 (사등동)경유<NA>9648L2023-10-30
5공군제3훈련비행단경상남도 사천시 사천읍 사천대로 1891-46경유항공기, 항공기부품, 건설기계6000L2023-10-30
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