Overview

Dataset statistics

Number of variables9
Number of observations60
Missing cells35
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory82.2 B

Variable types

DateTime1
Numeric8

Dataset

Description전력시장에 참여하는 신재생에너지발전기의 전력거래량을 신에너지, 재생에너지로 나누어 월별로 제공합니다. 연료전지, 석탄가스화, 태양, 풍력, 수력, 해양, 바이오, 폐기물의 전력거래량을 제공합니다. 단위 : MWh
Author한국전력거래소
URLhttps://www.data.go.kr/data/15069375/fileData.do

Alerts

(신에너지)연료전지 is highly overall correlated with (재생에너지)태양 and 1 other fieldsHigh correlation
(재생에너지)태양 is highly overall correlated with (신에너지)연료전지 and 1 other fieldsHigh correlation
(재생에너지)풍력 is highly overall correlated with (재생에너지)수력High correlation
(재생에너지)수력 is highly overall correlated with (재생에너지)풍력High correlation
(재생에너지)바이오 is highly overall correlated with (신에너지)연료전지 and 1 other fieldsHigh correlation
(신에너지)석탄가스화 has 8 (13.3%) missing valuesMissing
(재생에너지)폐기물 has 27 (45.0%) missing valuesMissing
기간 has unique valuesUnique
(신에너지)연료전지 has unique valuesUnique
(재생에너지)태양 has unique valuesUnique
(재생에너지)풍력 has unique valuesUnique
(재생에너지)수력 has unique valuesUnique
(재생에너지)해양 has unique valuesUnique
(재생에너지)바이오 has unique valuesUnique
(신에너지)석탄가스화 has 1 (1.7%) zerosZeros

Reproduction

Analysis started2023-12-12 17:25:01.922161
Analysis finished2023-12-12 17:25:08.890508
Duration6.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기간
Date

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
Minimum2017-01-01 00:00:00
Maximum2021-12-01 00:00:00
2023-12-13T02:25:08.951827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:09.107755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

(신에너지)연료전지
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224843.72
Minimum103661
Maximum446928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:25:09.258800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103661
5-th percentile115728.55
Q1135667.25
median184494.5
Q3322021.25
95-th percentile410999.45
Maximum446928
Range343267
Interquartile range (IQR)186354

Descriptive statistics

Standard deviation104024.57
Coefficient of variation (CV)0.46265276
Kurtosis-1.0083716
Mean224843.72
Median Absolute Deviation (MAD)59265
Skewness0.67942289
Sum13490623
Variance1.082111 × 1010
MonotonicityNot monotonic
2023-12-13T02:25:09.421862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113345 1
 
1.7%
183580 1
 
1.7%
199324 1
 
1.7%
188968 1
 
1.7%
208444 1
 
1.7%
211695 1
 
1.7%
205091 1
 
1.7%
237597 1
 
1.7%
252125 1
 
1.7%
289214 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
103661 1
1.7%
112625 1
1.7%
113345 1
1.7%
115854 1
1.7%
115885 1
1.7%
119065 1
1.7%
119524 1
1.7%
123679 1
1.7%
124845 1
1.7%
125614 1
1.7%
ValueCountFrequency (%)
446928 1
1.7%
421610 1
1.7%
412110 1
1.7%
410941 1
1.7%
404919 1
1.7%
388847 1
1.7%
382494 1
1.7%
376253 1
1.7%
373006 1
1.7%
360599 1
1.7%

(신에너지)석탄가스화
Real number (ℝ)

MISSING  ZEROS 

Distinct52
Distinct (%)100.0%
Missing8
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean125791.52
Minimum0
Maximum213632
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:25:09.588007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14579.65
Q175351.75
median143196.5
Q3175965
95-th percentile196632.8
Maximum213632
Range213632
Interquartile range (IQR)100613.25

Descriptive statistics

Standard deviation61868.159
Coefficient of variation (CV)0.49183092
Kurtosis-0.93775942
Mean125791.52
Median Absolute Deviation (MAD)43456.5
Skewness-0.57289922
Sum6541159
Variance3.8276691 × 109
MonotonicityNot monotonic
2023-12-13T02:25:09.734000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66312 1
 
1.7%
191498 1
 
1.7%
189773 1
 
1.7%
213632 1
 
1.7%
203736 1
 
1.7%
76942 1
 
1.7%
66568 1
 
1.7%
142189 1
 
1.7%
173144 1
 
1.7%
190795 1
 
1.7%
Other values (42) 42
70.0%
(Missing) 8
 
13.3%
ValueCountFrequency (%)
0 1
1.7%
21 1
1.7%
11751 1
1.7%
16894 1
1.7%
17656 1
1.7%
44342 1
1.7%
46400 1
1.7%
48098 1
1.7%
49226 1
1.7%
49972 1
1.7%
ValueCountFrequency (%)
213632 1
1.7%
203736 1
1.7%
197702 1
1.7%
195758 1
1.7%
191498 1
1.7%
190795 1
1.7%
190565 1
1.7%
190148 1
1.7%
189773 1
1.7%
184237 1
1.7%

(재생에너지)태양
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347417.77
Minimum143226
Maximum662723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:25:09.857540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum143226
5-th percentile177996.9
Q1246058.5
median317715
Q3444778.5
95-th percentile630693.15
Maximum662723
Range519497
Interquartile range (IQR)198720

Descriptive statistics

Standard deviation137972.67
Coefficient of variation (CV)0.39713763
Kurtosis-0.50489864
Mean347417.77
Median Absolute Deviation (MAD)97163.5
Skewness0.67378884
Sum20845066
Variance1.9036458 × 1010
MonotonicityNot monotonic
2023-12-13T02:25:09.979723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143226 1
 
1.7%
374614 1
 
1.7%
333902 1
 
1.7%
278115 1
 
1.7%
241307 1
 
1.7%
247426 1
 
1.7%
341190 1
 
1.7%
506265 1
 
1.7%
571839 1
 
1.7%
497580 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
143226 1
1.7%
156219 1
1.7%
171421 1
1.7%
178343 1
1.7%
180615 1
1.7%
187584 1
1.7%
189477 1
1.7%
198727 1
1.7%
206775 1
1.7%
218989 1
1.7%
ValueCountFrequency (%)
662723 1
1.7%
634410 1
1.7%
633299 1
1.7%
630556 1
1.7%
583445 1
1.7%
571839 1
1.7%
561443 1
1.7%
530768 1
1.7%
506265 1
1.7%
498393 1
1.7%

(재생에너지)풍력
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226513.18
Minimum83200
Maximum432290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:25:10.357176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83200
5-th percentile103758.65
Q1151755.75
median225106.5
Q3288425.5
95-th percentile376130.9
Maximum432290
Range349090
Interquartile range (IQR)136669.75

Descriptive statistics

Standard deviation84458.228
Coefficient of variation (CV)0.37286231
Kurtosis-0.62233173
Mean226513.18
Median Absolute Deviation (MAD)70673.5
Skewness0.34089251
Sum13590791
Variance7.1331922 × 109
MonotonicityNot monotonic
2023-12-13T02:25:10.493992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
258098 1
 
1.7%
145920 1
 
1.7%
227368 1
 
1.7%
253789 1
 
1.7%
340868 1
 
1.7%
310783 1
 
1.7%
292093 1
 
1.7%
301555 1
 
1.7%
309315 1
 
1.7%
215983 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
83200 1
1.7%
101589 1
1.7%
103087 1
1.7%
103794 1
1.7%
118055 1
1.7%
120798 1
1.7%
127217 1
1.7%
130033 1
1.7%
131836 1
1.7%
134480 1
1.7%
ValueCountFrequency (%)
432290 1
1.7%
400086 1
1.7%
384983 1
1.7%
375665 1
1.7%
346385 1
1.7%
342087 1
1.7%
340868 1
1.7%
321650 1
1.7%
321186 1
1.7%
310783 1
1.7%

(재생에너지)수력
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260753.77
Minimum154869
Maximum789513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:25:10.626655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum154869
5-th percentile168039.65
Q1189927.5
median227037
Q3281698.75
95-th percentile470893.55
Maximum789513
Range634644
Interquartile range (IQR)91771.25

Descriptive statistics

Standard deviation115516.83
Coefficient of variation (CV)0.44301115
Kurtosis7.8163883
Mean260753.77
Median Absolute Deviation (MAD)44987.5
Skewness2.4985169
Sum15645226
Variance1.3344137 × 1010
MonotonicityNot monotonic
2023-12-13T02:25:10.784657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169407 1
 
1.7%
281318 1
 
1.7%
249806 1
 
1.7%
180976 1
 
1.7%
194809 1
 
1.7%
221183 1
 
1.7%
204011 1
 
1.7%
227248 1
 
1.7%
210183 1
 
1.7%
270951 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
154869 1
1.7%
161532 1
1.7%
166988 1
1.7%
168095 1
1.7%
169281 1
1.7%
169407 1
1.7%
169597 1
1.7%
171746 1
1.7%
173204 1
1.7%
173968 1
1.7%
ValueCountFrequency (%)
789513 1
1.7%
619664 1
1.7%
526194 1
1.7%
467983 1
1.7%
407992 1
1.7%
405669 1
1.7%
384516 1
1.7%
368689 1
1.7%
361740 1
1.7%
334999 1
1.7%

(재생에너지)해양
Real number (ℝ)

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39356.133
Minimum35192
Maximum44670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:25:10.930292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35192
5-th percentile36239.05
Q137748.75
median39074.5
Q341219.5
95-th percentile43362.8
Maximum44670
Range9478
Interquartile range (IQR)3470.75

Descriptive statistics

Standard deviation2207.2911
Coefficient of variation (CV)0.05608506
Kurtosis-0.53297149
Mean39356.133
Median Absolute Deviation (MAD)1548.5
Skewness0.32239039
Sum2361368
Variance4872133.9
MonotonicityNot monotonic
2023-12-13T02:25:11.079782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42525 1
 
1.7%
41774 1
 
1.7%
43471 1
 
1.7%
39107 1
 
1.7%
39024 1
 
1.7%
38549 1
 
1.7%
37796 1
 
1.7%
40315 1
 
1.7%
37814 1
 
1.7%
36993 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
35192 1
1.7%
35500 1
1.7%
35974 1
1.7%
36253 1
1.7%
36402 1
1.7%
36641 1
1.7%
36725 1
1.7%
36993 1
1.7%
37016 1
1.7%
37052 1
1.7%
ValueCountFrequency (%)
44670 1
1.7%
43471 1
1.7%
43454 1
1.7%
43358 1
1.7%
42756 1
1.7%
42525 1
1.7%
42464 1
1.7%
42096 1
1.7%
41926 1
1.7%
41774 1
1.7%

(재생에너지)바이오
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean339512.4
Minimum131709
Maximum617170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:25:11.229421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131709
5-th percentile172441
Q1229264
median319062
Q3425020.25
95-th percentile593953.85
Maximum617170
Range485461
Interquartile range (IQR)195756.25

Descriptive statistics

Standard deviation133448.01
Coefficient of variation (CV)0.39305783
Kurtosis-0.78462145
Mean339512.4
Median Absolute Deviation (MAD)94203.5
Skewness0.50356851
Sum20370744
Variance1.7808371 × 1010
MonotonicityNot monotonic
2023-12-13T02:25:11.422478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155322 1
 
1.7%
399802 1
 
1.7%
384132 1
 
1.7%
378547 1
 
1.7%
423138 1
 
1.7%
322928 1
 
1.7%
359279 1
 
1.7%
405030 1
 
1.7%
337732 1
 
1.7%
309081 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
131709 1
1.7%
146135 1
1.7%
155322 1
1.7%
173342 1
1.7%
182509 1
1.7%
192703 1
1.7%
194630 1
1.7%
199516 1
1.7%
203694 1
1.7%
204162 1
1.7%
ValueCountFrequency (%)
617170 1
1.7%
609279 1
1.7%
606282 1
1.7%
593305 1
1.7%
575922 1
1.7%
535830 1
1.7%
532242 1
1.7%
530065 1
1.7%
507157 1
1.7%
478736 1
1.7%

(재생에너지)폐기물
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)100.0%
Missing27
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean853070.73
Minimum627848
Maximum962185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:25:11.576682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum627848
5-th percentile744514.6
Q1809602
median868709
Q3912031
95-th percentile957433.2
Maximum962185
Range334337
Interquartile range (IQR)102429

Descriptive statistics

Standard deviation75657.857
Coefficient of variation (CV)0.088688845
Kurtosis0.91057686
Mean853070.73
Median Absolute Deviation (MAD)55246
Skewness-0.78206434
Sum28151334
Variance5.7241114 × 109
MonotonicityNot monotonic
2023-12-13T02:25:11.794863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
811049 1
 
1.7%
872235 1
 
1.7%
936999 1
 
1.7%
956964 1
 
1.7%
917970 1
 
1.7%
962185 1
 
1.7%
867560 1
 
1.7%
958137 1
 
1.7%
752417 1
 
1.7%
869245 1
 
1.7%
Other values (23) 23
38.3%
(Missing) 27
45.0%
ValueCountFrequency (%)
627848 1
1.7%
732661 1
1.7%
752417 1
1.7%
760410 1
1.7%
773887 1
1.7%
783995 1
1.7%
786532 1
1.7%
792575 1
1.7%
809602 1
1.7%
811049 1
1.7%
ValueCountFrequency (%)
962185 1
1.7%
958137 1
1.7%
956964 1
1.7%
939754 1
1.7%
938145 1
1.7%
936999 1
1.7%
923394 1
1.7%
917970 1
1.7%
912031 1
1.7%
886176 1
1.7%

Interactions

2023-12-13T02:25:07.811372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.156185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.891081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.687638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:04.427212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.653402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.385949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.099359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.884739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.253782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.004645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.783394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:04.559582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.767564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.486150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.190104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.980722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.335903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.089730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.866950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.020547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.850753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.588499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.289021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:08.068493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.437393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.167260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.949837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.126159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.935746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.666256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.359438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:08.163708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.537577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.273750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:04.046542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.240576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.035632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.768984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.431379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:08.269385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.634528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.390190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:04.142319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.345415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.124142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.854319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.516535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:08.386325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.715364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.504696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:04.240662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.447773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.211031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.935438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.624551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:08.484115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:02.793775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:03.592283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:04.322193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:05.543811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:06.294021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.008853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:25:07.725589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:25:11.906927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기간(신에너지)연료전지(신에너지)석탄가스화(재생에너지)태양(재생에너지)풍력(재생에너지)수력(재생에너지)해양(재생에너지)바이오(재생에너지)폐기물
기간1.0001.0001.0001.0001.0001.0001.0001.0001.000
(신에너지)연료전지1.0001.0000.3650.8360.5360.3950.0000.8350.458
(신에너지)석탄가스화1.0000.3651.0000.3630.0000.0000.3970.0000.544
(재생에너지)태양1.0000.8360.3631.0000.0000.2700.0000.7660.396
(재생에너지)풍력1.0000.5360.0000.0001.0000.3200.0000.6500.000
(재생에너지)수력1.0000.3950.0000.2700.3201.0000.1730.0760.131
(재생에너지)해양1.0000.0000.3970.0000.0000.1731.0000.5970.413
(재생에너지)바이오1.0000.8350.0000.7660.6500.0760.5971.0000.155
(재생에너지)폐기물1.0000.4580.5440.3960.0000.1310.4130.1551.000
2023-12-13T02:25:12.106200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(신에너지)연료전지(신에너지)석탄가스화(재생에너지)태양(재생에너지)풍력(재생에너지)수력(재생에너지)해양(재생에너지)바이오(재생에너지)폐기물
(신에너지)연료전지1.0000.3920.8390.3480.185-0.4930.9210.420
(신에너지)석탄가스화0.3921.0000.2710.444-0.1410.0210.3720.028
(재생에너지)태양0.8390.2711.0000.0600.347-0.4250.7890.150
(재생에너지)풍력0.3480.4440.0601.000-0.596-0.0970.2620.151
(재생에너지)수력0.185-0.1410.347-0.5961.000-0.0850.2140.328
(재생에너지)해양-0.4930.021-0.425-0.097-0.0851.000-0.413-0.012
(재생에너지)바이오0.9210.3720.7890.2620.214-0.4131.0000.402
(재생에너지)폐기물0.4200.0280.1500.1510.328-0.0120.4021.000

Missing values

2023-12-13T02:25:08.607068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:25:08.732048image/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.
2023-12-13T02:25:08.823564image/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

기간(신에너지)연료전지(신에너지)석탄가스화(재생에너지)태양(재생에너지)풍력(재생에너지)수력(재생에너지)해양(재생에너지)바이오(재생에너지)폐기물
02017-01-011133454434214322625809816940742525155322875658
12017-02-011036611689418061525562515486937445131709752417
22017-03-0111588510685422551116602817174644670173342773887
32017-04-0111262514251524195619485419496042096146135732661
42017-05-011158547953426941414496123408143454194630813463
52017-06-01125614492262498698320020071739042203694792575
62017-07-011259731765618758412721732366739271192703866454
72017-08-0112367915585021898910379452619439383239208881133
82017-09-0111906514387822047711805525005440169226228868709
92017-10-01124845<NA>18947714063420134040271246976879989
기간(신에너지)연료전지(신에너지)석탄가스화(재생에너지)태양(재생에너지)풍력(재생에너지)수력(재생에너지)해양(재생에너지)바이오(재생에너지)폐기물
502021-03-0137625316343458344526893019718942756575922<NA>
512021-04-0137300619575866272326870923235639429532242<NA>
522021-05-0138249413312363441027283932765939037609279<NA>
532021-06-01354110063329913003338451635974478736<NA>
542021-07-013888471175163055612079836174037169606282<NA>
552021-08-0141094117560753076815370128284137016593305<NA>
562021-09-0140491918423749839321277429134237977530065<NA>
572021-10-014216109964156144320328021023637052535830<NA>
582021-11-0141211016892444460434208717396836253473284<NA>
592021-12-0144692817703941897443229017743037607617170<NA>