Overview

Dataset statistics

Number of variables4
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory34.8 B

Variable types

Categorical1
Text3

Dataset

Description한국서부발전(주)_신재생에너지사업현황
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2842

Alerts

사업명 has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:02:36.062951
Analysis finished2024-01-09 21:02:36.410342
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분 명
Categorical

Distinct3
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size508.0 B
운영
23 
개발
20 
건설
4 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영 23
48.9%
개발 20
42.6%
건설 4
 
8.5%

Length

2024-01-10T06:02:36.460210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:02:36.539364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영 23
48.9%
개발 20
42.6%
건설 4
 
8.5%

사업명
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2024-01-10T06:02:36.740126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.1276596
Min length4

Characters and Unicode

Total characters429
Distinct characters112
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row태안 IGCC
2nd row화순풍력 ESS
3rd row평택 #2 바이오중유
4th row태안#1~4
5th row태안화력 #2~4 유기성고형연료
ValueCountFrequency (%)
태양광 11
 
9.8%
수상태양광 5
 
4.5%
세종시 5
 
4.5%
태안본부 4
 
3.6%
풍력 4
 
3.6%
1단계 3
 
2.7%
양구 3
 
2.7%
ess 3
 
2.7%
서인천 3
 
2.7%
2차 3
 
2.7%
Other values (58) 68
60.7%
2024-01-10T06:02:37.121356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65
 
15.2%
태 24
 
5.6%
양 19
 
4.4%
광 16
 
3.7%
수 14
 
3.3%
부 12
 
2.8%
지 11
 
2.6%
력 10
 
2.3%
안 9
 
2.1%
본 9
 
2.1%
Other values (102) 240
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 323
75.3%
Space Separator 65
 
15.2%
Decimal Number 19
 
4.4%
Uppercase Letter 17
 
4.0%
Other Punctuation 3
 
0.7%
Math Symbol 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
태 24
 
7.4%
양 19
 
5.9%
광 16
 
5.0%
수 14
 
4.3%
부 12
 
3.7%
지 11
 
3.4%
력 10
 
3.1%
안 9
 
2.8%
본 9
 
2.8%
상 8
 
2.5%
Other values (87) 191
59.1%
Uppercase Letter
ValueCountFrequency (%)
S 6
35.3%
E 3
17.6%
I 2
 
11.8%
C 2
 
11.8%
D 1
 
5.9%
K 1
 
5.9%
G 1
 
5.9%
F 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
2 9
47.4%
1 7
36.8%
4 2
 
10.5%
3 1
 
5.3%
Space Separator
ValueCountFrequency (%)
65
100.0%
Other Punctuation
ValueCountFrequency (%)
# 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 323
75.3%
Common 89
 
20.7%
Latin 17
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
태 24
 
7.4%
양 19
 
5.9%
광 16
 
5.0%
수 14
 
4.3%
부 12
 
3.7%
지 11
 
3.4%
력 10
 
3.1%
안 9
 
2.8%
본 9
 
2.8%
상 8
 
2.5%
Other values (87) 191
59.1%
Latin
ValueCountFrequency (%)
S 6
35.3%
E 3
17.6%
I 2
 
11.8%
C 2
 
11.8%
D 1
 
5.9%
K 1
 
5.9%
G 1
 
5.9%
F 1
 
5.9%
Common
ValueCountFrequency (%)
65
73.0%
2 9
 
10.1%
1 7
 
7.9%
# 3
 
3.4%
4 2
 
2.2%
~ 2
 
2.2%
3 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 323
75.3%
ASCII 106
 
24.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65
61.3%
2 9
 
8.5%
1 7
 
6.6%
S 6
 
5.7%
# 3
 
2.8%
E 3
 
2.8%
I 2
 
1.9%
C 2
 
1.9%
4 2
 
1.9%
~ 2
 
1.9%
Other values (5) 5
 
4.7%
Hangul
ValueCountFrequency (%)
태 24
 
7.4%
양 19
 
5.9%
광 16
 
5.0%
수 14
 
4.3%
부 12
 
3.7%
지 11
 
3.4%
력 10
 
3.1%
안 9
 
2.8%
본 9
 
2.8%
상 8
 
2.5%
Other values (87) 191
59.1%

소재지
Text

Distinct30
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Memory size508.0 B
2024-01-10T06:02:37.361757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length11.468085
Min length1

Characters and Unicode

Total characters539
Distinct characters94
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

Unique24 ?
Unique (%)51.1%

Sample

1st row충남 태안군 태안읍 원북면
2nd row전남 화순군 동면 청궁리
3rd row경기도 평택시 포승읍
4th row7
5th row충남 태안군 태안읍 원북면
ValueCountFrequency (%)
충남 12
 
7.9%
태안읍 11
 
7.2%
원북면 11
 
7.2%
태안군 11
 
7.2%
전남 8
 
5.3%
경기도 5
 
3.3%
전북 5
 
3.3%
세종특별자치시 5
 
3.3%
서구 4
 
2.6%
강원도 4
 
2.6%
Other values (60) 76
50.0%
2024-01-10T06:02:37.694397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
105
19.5%
안 27
 
5.0%
남 22
 
4.1%
태 22
 
4.1%
시 21
 
3.9%
면 19
 
3.5%
군 18
 
3.3%
읍 17
 
3.2%
원 16
 
3.0%
북 16
 
3.0%
Other values (84) 256
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 420
77.9%
Space Separator 105
 
19.5%
Decimal Number 11
 
2.0%
Other Punctuation 2
 
0.4%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 27
 
6.4%
남 22
 
5.2%
태 22
 
5.2%
시 21
 
5.0%
면 19
 
4.5%
군 18
 
4.3%
읍 17
 
4.0%
원 16
 
3.8%
북 16
 
3.8%
전 15
 
3.6%
Other values (76) 227
54.0%
Decimal Number
ValueCountFrequency (%)
1 3
27.3%
3 3
27.3%
6 2
18.2%
2 2
18.2%
7 1
 
9.1%
Space Separator
ValueCountFrequency (%)
105
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 420
77.9%
Common 119
 
22.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 27
 
6.4%
남 22
 
5.2%
태 22
 
5.2%
시 21
 
5.0%
면 19
 
4.5%
군 18
 
4.3%
읍 17
 
4.0%
원 16
 
3.8%
북 16
 
3.8%
전 15
 
3.6%
Other values (76) 227
54.0%
Common
ValueCountFrequency (%)
105
88.2%
1 3
 
2.5%
3 3
 
2.5%
6 2
 
1.7%
2 2
 
1.7%
. 2
 
1.7%
~ 1
 
0.8%
7 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 420
77.9%
ASCII 119
 
22.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
105
88.2%
1 3
 
2.5%
3 3
 
2.5%
6 2
 
1.7%
2 2
 
1.7%
. 2
 
1.7%
~ 1
 
0.8%
7 1
 
0.8%
Hangul
ValueCountFrequency (%)
안 27
 
6.4%
남 22
 
5.2%
태 22
 
5.2%
시 21
 
5.0%
면 19
 
4.5%
군 18
 
4.3%
읍 17
 
4.0%
원 16
 
3.8%
북 16
 
3.8%
전 15
 
3.6%
Other values (76) 227
54.0%

용량(MW)
Text

Distinct39
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2024-01-10T06:02:37.891684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.8297872
Min length1

Characters and Unicode

Total characters133
Distinct characters16
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

Unique31 ?
Unique (%)66.0%

Sample

1st row346
2nd row4
3rd row70
4th row8 우드펠릿
5th row75
ValueCountFrequency (%)
20 2
 
4.2%
15 2
 
4.2%
1.84 2
 
4.2%
60 2
 
4.2%
100 2
 
4.2%
1.5 2
 
4.2%
16 2
 
4.2%
5 2
 
4.2%
18 1
 
2.1%
0.26 1
 
2.1%
Other values (30) 30
62.5%
2024-01-10T06:02:38.208703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 20
15.0%
0 19
14.3%
. 18
13.5%
5 13
9.8%
2 11
8.3%
6 10
7.5%
4 9
6.8%
8 8
 
6.0%
9 8
 
6.0%
3 8
 
6.0%
Other values (6) 9
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110
82.7%
Other Punctuation 18
 
13.5%
Other Letter 4
 
3.0%
Space Separator 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20
18.2%
0 19
17.3%
5 13
11.8%
2 11
10.0%
6 10
9.1%
4 9
8.2%
8 8
 
7.3%
9 8
 
7.3%
3 8
 
7.3%
7 4
 
3.6%
Other Letter
ValueCountFrequency (%)
우 1
25.0%
드 1
25.0%
펠 1
25.0%
릿 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129
97.0%
Hangul 4
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20
15.5%
0 19
14.7%
. 18
14.0%
5 13
10.1%
2 11
8.5%
6 10
7.8%
4 9
7.0%
8 8
 
6.2%
9 8
 
6.2%
3 8
 
6.2%
Other values (2) 5
 
3.9%
Hangul
ValueCountFrequency (%)
우 1
25.0%
드 1
25.0%
펠 1
25.0%
릿 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129
97.0%
Hangul 4
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20
15.5%
0 19
14.7%
. 18
14.0%
5 13
10.1%
2 11
8.5%
6 10
7.8%
4 9
7.0%
8 8
 
6.2%
9 8
 
6.2%
3 8
 
6.2%
Other values (2) 5
 
3.9%
Hangul
ValueCountFrequency (%)
우 1
25.0%
드 1
25.0%
펠 1
25.0%
릿 1
25.0%

Correlations

2024-01-10T06:02:38.294734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분 명사업명소재지용량(MW)
구분 명1.0001.0000.1110.753
사업명1.0001.0001.0001.000
소재지0.1111.0001.0000.000
용량(MW)0.7531.0000.0001.000

Missing values

2024-01-10T06:02:36.292668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:02:36.375866image/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

구분 명사업명소재지용량(MW)
0운영태안 IGCC충남 태안군 태안읍 원북면346
1운영화순풍력 ESS전남 화순군 동면 청궁리4
2운영평택 #2 바이오중유경기도 평택시 포승읍70
3운영태안#1~478 우드펠릿
4운영태안화력 #2~4 유기성고형연료충남 태안군 태안읍 원북면75
5운영화순풍력전남 화순군 동면 청궁리16
6운영서인천 2차 연료전지인천광역시 서구 장도로5
7운영서인천 1차 연료전지인천광역시 서구 장도로11.2
8운영태안본부 수상충남 태안군 태안읍 원북면1.84
9운영평택본부경기도 평택시 포승읍0.45
구분 명사업명소재지용량(MW)
37개발장흥풍력 및 ESS전남 장흥군 부산면 지천리16
38개발발전부지 태양광 ESS충남 태안군 태안읍 원북면25
39개발수도권매립지 태양광인천 서구 오류동100
40개발대산 수상태양광충남 서산시 대산읍15
41개발계화조류지 수상태양광전북 부안 계화면15
42개발청호지 수상태양광전북 부안 하서면32
43개발나주호 수상태양광전남 나주시 다도면38
44개발이원호 수상태양광충남 태안군 태안읍 원북면40
45개발태안본부 회사장 태양광충남 태안군 태안읍 원북면87
46개발군산본부 태양광전북 군산시 구암 3.1로1.84