Overview

Dataset statistics

Number of variables3
Number of observations238
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory26.5 B

Variable types

Categorical1
Numeric2

Dataset

Descriptionㅇ 석유 등 화석연료 및 원자력 중심인 우리나라의 1차 에너지중에서 신재생에너지의 공급량 변화 추이는 신재생에너지 실행계획 및 보급관련 정책수립을 위한 필수자료로 활용ㅇ 신재생에너지법 개정("19.10월)으로 재생에너지 중 비재생폐기물 제외됨에 따라 신재생에너지 생산량부터 비재생 폐기물을 제외한 신재생에너지 생산량 발표* 2016년부터 비재생 폐기물 생산량 전체 제외한 통계(구분, 연도, 생산량(toe))
Author산업통상자원부
URLhttps://www.data.go.kr/data/3039977/fileData.do

Alerts

생산량 has 35 (14.7%) zerosZeros

Reproduction

Analysis started2024-03-14 20:45:36.273375
Analysis finished2024-03-14 20:45:38.031845
Duration1.76 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct36
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
태양열
 
7
바이오-흑액
 
7
태양광-자가용
 
7
풍력-사업용
 
7
풍력-자가용
 
7
Other values (31)
203 

Length

Max length15
Median length11
Mean length7.592437
Min length2

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st row태양열
2nd row태양광-사업용
3rd row태양광-자가용
4th row풍력-사업용
5th row풍력-자가용

Common Values

ValueCountFrequency (%)
태양열 7
 
2.9%
바이오-흑액 7
 
2.9%
태양광-자가용 7
 
2.9%
풍력-사업용 7
 
2.9%
풍력-자가용 7
 
2.9%
수력-사업용 7
 
2.9%
수력-자가용 7
 
2.9%
바이오-임산연료 7
 
2.9%
지열 7
 
2.9%
수열 7
 
2.9%
Other values (26) 168
70.6%

Length

2024-03-15T05:45:38.207987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태양열 7
 
2.9%
태양광-사업용 7
 
2.9%
연료전지-사업용 7
 
2.9%
폐기물-폐목재 7
 
2.9%
폐기물-정제연료유 7
 
2.9%
폐기물-srf 7
 
2.9%
폐기물-rdf/rpf/tdf 7
 
2.9%
바이오-폐목재 7
 
2.9%
폐기물-생활폐기물 7
 
2.9%
연료전지-자가용 7
 
2.9%
Other values (26) 168
70.6%

연도
Real number (ℝ)

Distinct7
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-15T05:45:38.481995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.004215
Coefficient of variation (CV)0.00099267705
Kurtosis-1.2510233
Mean2019
Median Absolute Deviation (MAD)2
Skewness0
Sum480522
Variance4.0168776
MonotonicityIncreasing
2024-03-15T05:45:38.743351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2016 34
14.3%
2017 34
14.3%
2018 34
14.3%
2019 34
14.3%
2020 34
14.3%
2021 34
14.3%
2022 34
14.3%
ValueCountFrequency (%)
2016 34
14.3%
2017 34
14.3%
2018 34
14.3%
2019 34
14.3%
2020 34
14.3%
2021 34
14.3%
2022 34
14.3%
ValueCountFrequency (%)
2022 34
14.3%
2021 34
14.3%
2020 34
14.3%
2019 34
14.3%
2018 34
14.3%
2017 34
14.3%
2016 34
14.3%

생산량
Real number (ℝ)

ZEROS 

Distinct204
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327506.14
Minimum0
Maximum5683463
Zeros35
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-15T05:45:38.966280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114901.75
median156215
Q3408443
95-th percentile1104475.1
Maximum5683463
Range5683463
Interquartile range (IQR)393541.25

Descriptive statistics

Standard deviation613931.76
Coefficient of variation (CV)1.8745657
Kurtosis36.348063
Mean327506.14
Median Absolute Deviation (MAD)153781.5
Skewness5.2745047
Sum77946461
Variance3.769122 × 1011
MonotonicityNot monotonic
2024-03-15T05:45:39.214178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
14.7%
28495 1
 
0.4%
689957 1
 
0.4%
10858 1
 
0.4%
142876 1
 
0.4%
1325675 1
 
0.4%
72501 1
 
0.4%
197589 1
 
0.4%
154832 1
 
0.4%
501406 1
 
0.4%
Other values (194) 194
81.5%
ValueCountFrequency (%)
0 35
14.7%
506 1
 
0.4%
565 1
 
0.4%
594 1
 
0.4%
647 1
 
0.4%
666 1
 
0.4%
793 1
 
0.4%
869 1
 
0.4%
2241 1
 
0.4%
2332 1
 
0.4%
ValueCountFrequency (%)
5683463 1
0.4%
4567632 1
0.4%
3504379 1
0.4%
2504790 1
0.4%
1836137 1
0.4%
1787454 1
0.4%
1750684 1
0.4%
1543390 1
0.4%
1486488 1
0.4%
1325675 1
0.4%

Interactions

2024-03-15T05:45:36.962776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:45:36.424407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:45:37.277860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:45:36.697730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:45:39.410791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연도생산량
구분1.0000.0000.688
연도0.0001.0000.000
생산량0.6880.0001.000
2024-03-15T05:45:39.890911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도생산량구분
연도1.0000.0570.000
생산량0.0571.0000.319
구분0.0000.3191.000

Missing values

2024-03-15T05:45:37.637841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:45:37.926155image/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

구분연도생산량
0태양열201628495
1태양광-사업용2016947609
2태양광-자가용2016235699
3풍력-사업용2016352953
4풍력-자가용20162387
5수력-사업용2016602578
6수력-자가용2016666
7해양2016104562
8지열2016162047
9수열20165989
구분연도생산량
228폐기물-생활폐기물2022408095
229폐기물-대형도시쓰레기20220
230폐기물-시멘트킬른보조연료2022318115
231폐기물-RDF/RPF/TDF20220
232폐기물-SRF2022224565
233폐기물-정제연료유20220
234폐기물-폐목재20220
235연료전지-사업용20221135223
236연료전지-자가용202218307
237IGCC-사업용2022418508