Overview

Dataset statistics

Number of variables7
Number of observations3654
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory214.2 KiB
Average record size in memory60.0 B

Variable types

Categorical2
Text1
Numeric4

Dataset

Description기초지자체별 신재생에너지원별 생산량, 발전량, 보급용량(신규, 누적) 데이터주1) 수력은 양수발전 제외하며, "03년부터 수력에 대수력(10MW) 포함주2) "11년부터 폐목재는 폐기물에서 바이오로 분류변경주3) "11년부터 TDF 추가주4) "14년부터 RDF/RPF/TDF는 SRF로 대체 조사주5) "14년부터 우드칩, 목재펠릿 중 일부는 Bio-SRF로 대체 분류주6) "15년부터 대형도시쓰레기는 생활폐기물로 포함주7) 신에너지 및 재생에너지 개발·이용·보급 촉진법 개정("19.10.01 시행)에 따라 폐기물에너지 중 비재생폐기물은 제외
Author한국에너지공단
URLhttps://www.data.go.kr/data/15086292/fileData.do

Alerts

생산량(toe) is highly overall correlated with 발전량(MWh) and 2 other fieldsHigh correlation
발전량(MWh) is highly overall correlated with 생산량(toe) and 2 other fieldsHigh correlation
보급용량_발전_누적(kW) is highly overall correlated with 생산량(toe) and 2 other fieldsHigh correlation
보급용량_발전_신규(kW) is highly overall correlated with 생산량(toe) and 2 other fieldsHigh correlation
생산량(toe) has 1274 (34.9%) zerosZeros
발전량(MWh) has 2109 (57.7%) zerosZeros
보급용량_발전_누적(kW) has 2518 (68.9%) zerosZeros
보급용량_발전_신규(kW) has 3019 (82.6%) zerosZeros

Reproduction

Analysis started2024-03-16 04:11:46.736022
Analysis finished2024-03-16 04:11:57.603630
Duration10.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

광역
Categorical

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size28.7 KiB
경기
462 
서울
378 
경북
350 
전남
336 
강원
280 
Other values (12)
1848 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기 462
12.6%
서울 378
10.3%
경북 350
9.6%
전남 336
9.2%
강원 280
7.7%
경남 280
7.7%
부산 252
 
6.9%
충남 238
 
6.5%
전북 224
 
6.1%
충북 182
 
5.0%
Other values (7) 672
18.4%

Length

2024-03-16T13:11:57.772146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 462
12.6%
서울 378
10.3%
경북 350
9.6%
전남 336
9.2%
강원 280
7.7%
경남 280
7.7%
부산 252
 
6.9%
충남 238
 
6.5%
전북 224
 
6.1%
충북 182
 
5.0%
Other values (7) 672
18.4%

기초
Text

Distinct224
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size28.7 KiB
2024-03-16T13:11:58.554646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8199234
Min length2

Characters and Unicode

Total characters10304
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row종로구
3rd row중구
4th row용산구
5th row성동구
ValueCountFrequency (%)
기타 224
 
6.1%
중구 84
 
2.3%
동구 84
 
2.3%
서구 70
 
1.9%
남구 56
 
1.5%
북구 56
 
1.5%
강서구 28
 
0.8%
고성군 28
 
0.8%
남원시 14
 
0.4%
익산시 14
 
0.4%
Other values (214) 2996
82.0%
2024-03-16T13:11:59.892778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 1190
 
11.5%
시 1092
 
10.6%
구 1050
 
10.2%
천 322
 
3.1%
주 308
 
3.0%
기 252
 
2.4%
성 252
 
2.4%
산 252
 
2.4%
양 252
 
2.4%
동 238
 
2.3%
Other values (124) 5096
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10304
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 1190
 
11.5%
시 1092
 
10.6%
구 1050
 
10.2%
천 322
 
3.1%
주 308
 
3.0%
기 252
 
2.4%
성 252
 
2.4%
산 252
 
2.4%
양 252
 
2.4%
동 238
 
2.3%
Other values (124) 5096
49.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10304
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 1190
 
11.5%
시 1092
 
10.6%
구 1050
 
10.2%
천 322
 
3.1%
주 308
 
3.0%
기 252
 
2.4%
성 252
 
2.4%
산 252
 
2.4%
양 252
 
2.4%
동 238
 
2.3%
Other values (124) 5096
49.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10304
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 1190
 
11.5%
시 1092
 
10.6%
구 1050
 
10.2%
천 322
 
3.1%
주 308
 
3.0%
기 252
 
2.4%
성 252
 
2.4%
산 252
 
2.4%
양 252
 
2.4%
동 238
 
2.3%
Other values (124) 5096
49.5%

에너지원
Categorical

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size28.7 KiB
신·재생에너지
261 
재생에너지
261 
신에너지
261 
태양열
261 
태양광
261 
Other values (9)
2349 

Length

Max length7
Median length5
Mean length3.2857143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신·재생에너지
2nd row신·재생에너지
3rd row신·재생에너지
4th row신·재생에너지
5th row신·재생에너지

Common Values

ValueCountFrequency (%)
신·재생에너지 261
 
7.1%
재생에너지 261
 
7.1%
신에너지 261
 
7.1%
태양열 261
 
7.1%
태양광 261
 
7.1%
풍력 261
 
7.1%
수력 261
 
7.1%
해양 261
 
7.1%
지열 261
 
7.1%
수열 261
 
7.1%
Other values (4) 1044
28.6%

Length

2024-03-16T13:12:00.227173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신·재생에너지 261
 
7.1%
재생에너지 261
 
7.1%
신에너지 261
 
7.1%
태양열 261
 
7.1%
태양광 261
 
7.1%
풍력 261
 
7.1%
수력 261
 
7.1%
해양 261
 
7.1%
지열 261
 
7.1%
수열 261
 
7.1%
Other values (4) 1044
28.6%

생산량(toe)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1672
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25716.129
Minimum0
Maximum2539588
Zeros1274
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2024-03-16T13:12:00.506470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median136.5
Q36137.75
95-th percentile92633.2
Maximum2539588
Range2539588
Interquartile range (IQR)6137.75

Descriptive statistics

Standard deviation130351.88
Coefficient of variation (CV)5.0688763
Kurtosis160.66632
Mean25716.129
Median Absolute Deviation (MAD)136.5
Skewness11.455503
Sum93966734
Variance1.6991611 × 1010
MonotonicityNot monotonic
2024-03-16T13:12:00.836723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1274
34.9%
1 33
 
0.9%
15 22
 
0.6%
7 19
 
0.5%
6 14
 
0.4%
18 13
 
0.4%
4 13
 
0.4%
88 12
 
0.3%
3 12
 
0.3%
16 11
 
0.3%
Other values (1662) 2231
61.1%
ValueCountFrequency (%)
0 1274
34.9%
1 33
 
0.9%
2 6
 
0.2%
3 12
 
0.3%
4 13
 
0.4%
5 1
 
< 0.1%
6 14
 
0.4%
7 19
 
0.5%
8 5
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
2539588 1
< 0.1%
2515875 1
< 0.1%
2163189 1
< 0.1%
1782795 1
< 0.1%
1754851 1
< 0.1%
1730425 1
< 0.1%
1692575 1
< 0.1%
1671606 1
< 0.1%
1640544 1
< 0.1%
1453494 1
< 0.1%

발전량(MWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1120
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94737.788
Minimum0
Maximum10385213
Zeros2109
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2024-03-16T13:12:01.173079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38395
95-th percentile331446
Maximum10385213
Range10385213
Interquartile range (IQR)8395

Descriptive statistics

Standard deviation520142.3
Coefficient of variation (CV)5.4903361
Kurtosis169.81997
Mean94737.788
Median Absolute Deviation (MAD)0
Skewness11.749692
Sum3.4617188 × 108
Variance2.7054801 × 1011
MonotonicityNot monotonic
2024-03-16T13:12:01.789529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2109
57.7%
6 22
 
0.6%
64 16
 
0.4%
32 15
 
0.4%
26 9
 
0.2%
115 8
 
0.2%
19 7
 
0.2%
223 6
 
0.2%
383 6
 
0.2%
77 6
 
0.2%
Other values (1110) 1450
39.7%
ValueCountFrequency (%)
0 2109
57.7%
1 2
 
0.1%
2 4
 
0.1%
3 3
 
0.1%
4 2
 
0.1%
5 4
 
0.1%
6 22
 
0.6%
8 3
 
0.1%
10 2
 
0.1%
12 5
 
0.1%
ValueCountFrequency (%)
10385213 1
< 0.1%
10274015 1
< 0.1%
9023827 1
< 0.1%
7481402 1
< 0.1%
7189211 1
< 0.1%
6570279 1
< 0.1%
6479493 1
< 0.1%
6376158 1
< 0.1%
6203412 1
< 0.1%
5534009 1
< 0.1%

보급용량_발전_누적(kW)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct940
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36896.549
Minimum0
Maximum5674964
Zeros2518
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2024-03-16T13:12:02.038104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3314.5
95-th percentile168635.2
Maximum5674964
Range5674964
Interquartile range (IQR)314.5

Descriptive statistics

Standard deviation242613.39
Coefficient of variation (CV)6.5755036
Kurtosis273.70572
Mean36896.549
Median Absolute Deviation (MAD)0
Skewness15.200341
Sum1.3481999 × 108
Variance5.8861256 × 1010
MonotonicityNot monotonic
2024-03-16T13:12:02.310577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2518
68.9%
3 21
 
0.6%
20 9
 
0.2%
5 9
 
0.2%
3000 9
 
0.2%
4 7
 
0.2%
10 7
 
0.2%
2 7
 
0.2%
600 6
 
0.2%
1 6
 
0.2%
Other values (930) 1055
28.9%
ValueCountFrequency (%)
0 2518
68.9%
1 6
 
0.2%
2 7
 
0.2%
3 21
 
0.6%
4 7
 
0.2%
5 9
 
0.2%
6 4
 
0.1%
7 1
 
< 0.1%
8 3
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
5674964 1
< 0.1%
5057943 1
< 0.1%
4989276 1
< 0.1%
4238560 1
< 0.1%
4083961 1
< 0.1%
3921475 1
< 0.1%
3403244 1
< 0.1%
3219463 1
< 0.1%
2854218 1
< 0.1%
2784730 1
< 0.1%

보급용량_발전_신규(kW)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct436
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4162.3038
Minimum0
Maximum775912
Zeros3019
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2024-03-16T13:12:02.629864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile17573
Maximum775912
Range775912
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30637.361
Coefficient of variation (CV)7.3606739
Kurtosis312.89586
Mean4162.3038
Median Absolute Deviation (MAD)0
Skewness16.171489
Sum15209058
Variance9.3864787 × 108
MonotonicityNot monotonic
2024-03-16T13:12:03.398510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3019
82.6%
20 12
 
0.3%
10 8
 
0.2%
120 5
 
0.1%
6 5
 
0.1%
3 4
 
0.1%
3000 4
 
0.1%
30 4
 
0.1%
5 4
 
0.1%
40 4
 
0.1%
Other values (426) 585
 
16.0%
ValueCountFrequency (%)
0 3019
82.6%
1 1
 
< 0.1%
3 4
 
0.1%
5 4
 
0.1%
6 5
 
0.1%
8 1
 
< 0.1%
9 2
 
0.1%
10 8
 
0.2%
11 2
 
0.1%
12 2
 
0.1%
ValueCountFrequency (%)
775912 1
< 0.1%
696193 1
< 0.1%
570850 1
< 0.1%
560326 1
< 0.1%
555682 1
< 0.1%
510806 1
< 0.1%
407647 1
< 0.1%
321438 1
< 0.1%
295015 1
< 0.1%
288832 1
< 0.1%

Interactions

2024-03-16T13:11:54.925707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:51.729117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:52.806454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:53.852323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:55.089305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:52.103117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:52.996042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:54.157853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:55.269860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:52.407049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:53.259923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:54.458773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:55.464030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:52.609281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:53.510448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:54.750687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:12:03.689230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역에너지원생산량(toe)발전량(MWh)보급용량_발전_누적(kW)보급용량_발전_신규(kW)
광역1.0000.0000.1900.1670.0570.052
에너지원0.0001.0000.1240.1010.1500.110
생산량(toe)0.1900.1241.0000.9830.9180.910
발전량(MWh)0.1670.1010.9831.0000.9440.924
보급용량_발전_누적(kW)0.0570.1500.9180.9441.0000.976
보급용량_발전_신규(kW)0.0520.1100.9100.9240.9761.000
2024-03-16T13:12:03.951733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역에너지원
광역1.0000.000
에너지원0.0001.000
2024-03-16T13:12:04.229796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생산량(toe)발전량(MWh)보급용량_발전_누적(kW)보급용량_발전_신규(kW)광역에너지원
생산량(toe)1.0000.7880.5840.5230.0750.050
발전량(MWh)0.7881.0000.7440.6150.0650.041
보급용량_발전_누적(kW)0.5840.7441.0000.7670.0220.061
보급용량_발전_신규(kW)0.5230.6150.7671.0000.0200.044
광역0.0750.0650.0220.0201.0000.000
에너지원0.0500.0410.0610.0440.0001.000

Missing values

2024-03-16T13:11:56.758393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:11:57.327643image/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

광역기초에너지원생산량(toe)발전량(MWh)보급용량_발전_누적(kW)보급용량_발전_신규(kW)
0서울서울신·재생에너지31702372317635399434688
1서울종로구신·재생에너지3582733461531540
2서울중구신·재생에너지321730672725784
3서울용산구신·재생에너지299337962779702
4서울성동구신·재생에너지5126109418669756
5서울광진구신·재생에너지554312714101271038
6서울동대문구신·재생에너지506199719042993
7서울중랑구신·재생에너지57801159997902842
8서울성북구신·재생에너지6323117239191854
9서울강북구신·재생에너지379576716364885
광역기초에너지원생산량(toe)발전량(MWh)보급용량_발전_누적(kW)보급용량_발전_신규(kW)
3644경남하동군IGCC0000
3645경남산청군IGCC0000
3646경남함양군IGCC0000
3647경남거창군IGCC0000
3648경남합천군IGCC0000
3649경남기타IGCC0000
3650제주제주도IGCC0000
3651제주제주시IGCC0000
3652제주서귀포시IGCC0000
3653제주기타IGCC0000