Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory106.0 B

Variable types

DateTime1
Numeric10

Dataset

Description전력시장에 참여하는 신재생에너지 발전기의 전력거래량 합계를 제공합니다. - 항목 : 날짜/시간, 태양광, 폐기물, 풍력, 해양, 바이오 - 단위 : MWh - 기간 : 2012~2022년 3월
URLhttps://www.data.go.kr/data/15069372/fileData.do

Alerts

연료전지 is highly overall correlated with 석탄가스화복합화력(IGCC) and 2 other fieldsHigh correlation
석탄가스화복합화력(IGCC) is highly overall correlated with 연료전지 and 1 other fieldsHigh correlation
태양광 is highly overall correlated with 신재생 합계High correlation
바이오에너지 is highly overall correlated with 연료전지 and 2 other fieldsHigh correlation
신재생 합계 is highly overall correlated with 연료전지 and 2 other fieldsHigh correlation
연료전지 has unique valuesUnique
풍력 has unique valuesUnique
수력 has unique valuesUnique
바이오에너지 has unique valuesUnique
신재생 합계 has unique valuesUnique
석탄가스화복합화력(IGCC) has 6553 (65.5%) zerosZeros
태양광 has 1631 (16.3%) zerosZeros
해양에너지 has 5481 (54.8%) zerosZeros
폐기물에너지 has 2436 (24.4%) zerosZeros

Reproduction

Analysis started2023-12-12 13:41:30.070864
Analysis finished2023-12-12 13:41:45.599650
Duration15.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

거래일
Date

Distinct3510
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2012-01-01 00:00:00
Maximum2022-03-31 00:00:00
2023-12-12T22:41:45.666783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:45.832579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

거래시간
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.2631
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:45.956664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q318
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8961028
Coefficient of variation (CV)0.5623458
Kurtosis-1.1933537
Mean12.2631
Median Absolute Deviation (MAD)6
Skewness0.046420333
Sum122631
Variance47.556234
MonotonicityNot monotonic
2023-12-12T22:41:46.065971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 458
 
4.6%
8 448
 
4.5%
9 447
 
4.5%
10 441
 
4.4%
15 438
 
4.4%
13 438
 
4.4%
4 437
 
4.4%
6 433
 
4.3%
1 427
 
4.3%
2 425
 
4.2%
Other values (14) 5608
56.1%
ValueCountFrequency (%)
1 427
4.3%
2 425
4.2%
3 458
4.6%
4 437
4.4%
5 406
4.1%
6 433
4.3%
7 409
4.1%
8 448
4.5%
9 447
4.5%
10 441
4.4%
ValueCountFrequency (%)
24 374
3.7%
23 418
4.2%
22 393
3.9%
21 412
4.1%
20 393
3.9%
19 388
3.9%
18 407
4.1%
17 386
3.9%
16 395
4.0%
15 438
4.4%

연료전지
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.79164
Minimum27.074135
Maximum625.34344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:46.187136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.074135
5-th percentile42.323479
Q1104.507
median152.63096
Q3259.10286
95-th percentile560.55506
Maximum625.34344
Range598.26931
Interquartile range (IQR)154.59587

Descriptive statistics

Standard deviation159.54805
Coefficient of variation (CV)0.76050722
Kurtosis0.15738294
Mean209.79164
Median Absolute Deviation (MAD)62.579686
Skewness1.1480362
Sum2097916.4
Variance25455.581
MonotonicityNot monotonic
2023-12-12T22:41:46.298892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.312805 1
 
< 0.1%
44.702339 1
 
< 0.1%
562.705353 1
 
< 0.1%
619.94261 1
 
< 0.1%
190.189079 1
 
< 0.1%
124.798866 1
 
< 0.1%
118.983143 1
 
< 0.1%
105.682904 1
 
< 0.1%
194.134485 1
 
< 0.1%
557.314729 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
27.074135 1
< 0.1%
27.3925 1
< 0.1%
28.316945 1
< 0.1%
29.815425 1
< 0.1%
30.798108 1
< 0.1%
30.881469 1
< 0.1%
30.913677 1
< 0.1%
31.179712 1
< 0.1%
31.185241 1
< 0.1%
31.312096 1
< 0.1%
ValueCountFrequency (%)
625.343444 1
< 0.1%
621.648988 1
< 0.1%
621.414412 1
< 0.1%
621.182624 1
< 0.1%
620.985409 1
< 0.1%
620.968087 1
< 0.1%
620.921707 1
< 0.1%
620.770747 1
< 0.1%
620.688261 1
< 0.1%
620.101926 1
< 0.1%

석탄가스화복합화력(IGCC)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3408
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.044771
Minimum0
Maximum314.97312
Zeros6553
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:46.440975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3235.06546
95-th percentile272.03305
Maximum314.97312
Range314.97312
Interquartile range (IQR)235.06546

Descriptive statistics

Standard deviation117.75256
Coefficient of variation (CV)1.4010694
Kurtosis-1.4019447
Mean84.044771
Median Absolute Deviation (MAD)0
Skewness0.72681916
Sum840447.71
Variance13865.665
MonotonicityNot monotonic
2023-12-12T22:41:46.590447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6553
65.5%
239.43584 3
 
< 0.1%
261.52616 3
 
< 0.1%
234.74976 2
 
< 0.1%
275.51104 2
 
< 0.1%
270.23248 2
 
< 0.1%
196.56784 2
 
< 0.1%
233.86104 2
 
< 0.1%
246.31208 2
 
< 0.1%
267.0164 2
 
< 0.1%
Other values (3398) 3427
34.3%
ValueCountFrequency (%)
0.0 6553
65.5%
0.00112 1
 
< 0.1%
0.01624 1
 
< 0.1%
0.02128 1
 
< 0.1%
0.16968 1
 
< 0.1%
0.25312 1
 
< 0.1%
0.61656 1
 
< 0.1%
0.83608 1
 
< 0.1%
7.52024 1
 
< 0.1%
9.09944 1
 
< 0.1%
ValueCountFrequency (%)
314.97312 1
< 0.1%
312.8496 1
< 0.1%
309.26056 1
< 0.1%
307.66624 1
< 0.1%
306.45384 1
< 0.1%
306.01144 1
< 0.1%
304.69096 1
< 0.1%
304.07608 1
< 0.1%
303.97024 1
< 0.1%
302.24152 1
< 0.1%

태양광
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7919
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.74749
Minimum0
Maximum4144.9879
Zeros1631
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:46.737710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0461495
median50.44418
Q3441.16902
95-th percentile1427.2537
Maximum4144.9879
Range4144.9879
Interquartile range (IQR)441.12287

Descriptive statistics

Standard deviation517.03022
Coefficient of variation (CV)1.6169954
Kurtosis5.8973802
Mean319.74749
Median Absolute Deviation (MAD)50.44418
Skewness2.2707534
Sum3197474.9
Variance267320.25
MonotonicityNot monotonic
2023-12-12T22:41:46.855259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1631
 
16.3%
8e-05 99
 
1.0%
0.00016 77
 
0.8%
0.00014 42
 
0.4%
0.00022 38
 
0.4%
0.0003 32
 
0.3%
0.00024 32
 
0.3%
6e-05 31
 
0.3%
0.00032 17
 
0.2%
0.0002 12
 
0.1%
Other values (7909) 7989
79.9%
ValueCountFrequency (%)
0.0 1631
16.3%
6e-06 2
 
< 0.1%
8e-06 1
 
< 0.1%
2.4e-05 2
 
< 0.1%
2.9e-05 6
 
0.1%
3.8e-05 1
 
< 0.1%
4e-05 3
 
< 0.1%
4.8e-05 1
 
< 0.1%
5.8e-05 2
 
< 0.1%
6e-05 31
 
0.3%
ValueCountFrequency (%)
4144.987862 1
< 0.1%
3604.014003 1
< 0.1%
3506.150052 1
< 0.1%
3408.87439 1
< 0.1%
3391.063974 1
< 0.1%
3385.391963 1
< 0.1%
3330.208107 1
< 0.1%
3325.348529 1
< 0.1%
3311.37077 1
< 0.1%
3286.670586 1
< 0.1%

풍력
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.59199
Minimum0.344246
Maximum1224.3516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:46.995722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.344246
5-th percentile16.607148
Q172.219771
median167.77207
Q3313.41439
95-th percentile694.80808
Maximum1224.3516
Range1224.0073
Interquartile range (IQR)241.19461

Descriptive statistics

Standard deviation217.49797
Coefficient of variation (CV)0.93914289
Kurtosis2.4034001
Mean231.59199
Median Absolute Deviation (MAD)110.63278
Skewness1.5560193
Sum2315919.9
Variance47305.369
MonotonicityNot monotonic
2023-12-12T22:41:47.118727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251.675468 1
 
< 0.1%
72.197499 1
 
< 0.1%
129.512553 1
 
< 0.1%
971.405592 1
 
< 0.1%
336.754227 1
 
< 0.1%
45.675019 1
 
< 0.1%
27.353757 1
 
< 0.1%
171.020366 1
 
< 0.1%
356.379144 1
 
< 0.1%
188.553687 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.344246 1
< 0.1%
0.35824 1
< 0.1%
0.481227 1
< 0.1%
0.532478 1
< 0.1%
0.828405 1
< 0.1%
0.837079 1
< 0.1%
0.996636 1
< 0.1%
1.008241 1
< 0.1%
1.011686 1
< 0.1%
1.171012 1
< 0.1%
ValueCountFrequency (%)
1224.351556 1
< 0.1%
1224.157668 1
< 0.1%
1212.367205 1
< 0.1%
1199.401118 1
< 0.1%
1194.083779 1
< 0.1%
1192.887455 1
< 0.1%
1183.060468 1
< 0.1%
1169.513744 1
< 0.1%
1165.84992 1
< 0.1%
1161.078926 1
< 0.1%

수력
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.53873
Minimum36.023563
Maximum1462.2423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:47.245929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.023563
5-th percentile85.409143
Q1147.24187
median304.17375
Q3498.77689
95-th percentile812.72532
Maximum1462.2423
Range1426.2188
Interquartile range (IQR)351.53502

Descriptive statistics

Standard deviation247.29094
Coefficient of variation (CV)0.69750048
Kurtosis1.4527688
Mean354.53873
Median Absolute Deviation (MAD)167.63797
Skewness1.1697382
Sum3545387.3
Variance61152.807
MonotonicityNot monotonic
2023-12-12T22:41:47.369036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171.901137 1
 
< 0.1%
373.262608 1
 
< 0.1%
195.585503 1
 
< 0.1%
348.193159 1
 
< 0.1%
629.654014 1
 
< 0.1%
129.782625 1
 
< 0.1%
114.164324 1
 
< 0.1%
361.540634 1
 
< 0.1%
372.489349 1
 
< 0.1%
524.63628 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
36.023563 1
< 0.1%
37.384413 1
< 0.1%
41.214147 1
< 0.1%
43.535667 1
< 0.1%
45.995187 1
< 0.1%
47.705064 1
< 0.1%
48.910561 1
< 0.1%
49.560853 1
< 0.1%
50.01112 1
< 0.1%
51.763575 1
< 0.1%
ValueCountFrequency (%)
1462.242341 1
< 0.1%
1448.286258 1
< 0.1%
1440.354645 1
< 0.1%
1432.548609 1
< 0.1%
1410.699526 1
< 0.1%
1403.779296 1
< 0.1%
1400.4859 1
< 0.1%
1400.385818 1
< 0.1%
1398.366652 1
< 0.1%
1393.376028 1
< 0.1%

해양에너지
Real number (ℝ)

ZEROS 

Distinct4480
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.08596
Minimum0
Maximum262.9153
Zeros5481
Zeros (%)54.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:47.494582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3112.04248
95-th percentile217.69213
Maximum262.9153
Range262.9153
Interquartile range (IQR)112.04248

Descriptive statistics

Standard deviation78.076338
Coefficient of variation (CV)1.4435602
Kurtosis-0.21768776
Mean54.08596
Median Absolute Deviation (MAD)0
Skewness1.1241597
Sum540859.6
Variance6095.9146
MonotonicityNot monotonic
2023-12-12T22:41:47.626533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5481
54.8%
0.00072 6
 
0.1%
0.0072 5
 
0.1%
0.0036 4
 
< 0.1%
0.00288 4
 
< 0.1%
0.01728 3
 
< 0.1%
0.00504 3
 
< 0.1%
0.01368 3
 
< 0.1%
0.01512 3
 
< 0.1%
0.01008 3
 
< 0.1%
Other values (4470) 4485
44.9%
ValueCountFrequency (%)
0.0 5481
54.8%
9.6e-05 1
 
< 0.1%
0.000192 1
 
< 0.1%
0.000504 1
 
< 0.1%
0.00072 6
 
0.1%
0.00084 1
 
< 0.1%
0.00096 1
 
< 0.1%
0.001344 1
 
< 0.1%
0.00144 1
 
< 0.1%
0.001848 1
 
< 0.1%
ValueCountFrequency (%)
262.915296 1
< 0.1%
260.581608 1
< 0.1%
260.331288 1
< 0.1%
255.141096 1
< 0.1%
254.998632 1
< 0.1%
253.146936 1
< 0.1%
251.268696 1
< 0.1%
250.857768 1
< 0.1%
250.84752 1
< 0.1%
250.824504 1
< 0.1%

바이오에너지
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.20192
Minimum4.431888
Maximum1030.8343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:47.840358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.431888
5-th percentile38.36265
Q167.457258
median193.23728
Q3461.78415
95-th percentile797.0055
Maximum1030.8343
Range1026.4024
Interquartile range (IQR)394.32689

Descriptive statistics

Standard deviation246.16315
Coefficient of variation (CV)0.8511809
Kurtosis-0.3438435
Mean289.20192
Median Absolute Deviation (MAD)138.32019
Skewness0.85802015
Sum2892019.2
Variance60596.297
MonotonicityNot monotonic
2023-12-12T22:41:48.020483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.199353 1
 
< 0.1%
57.585708 1
 
< 0.1%
620.391271 1
 
< 0.1%
638.534106 1
 
< 0.1%
338.730429 1
 
< 0.1%
191.778085 1
 
< 0.1%
180.407629 1
 
< 0.1%
75.519327 1
 
< 0.1%
345.836347 1
 
< 0.1%
686.398736 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
4.431888 1
< 0.1%
4.442304 1
< 0.1%
5.180823 1
< 0.1%
5.511672 1
< 0.1%
5.747496 1
< 0.1%
5.781125 1
< 0.1%
6.09372 1
< 0.1%
6.375292 1
< 0.1%
6.571815 1
< 0.1%
6.603473 1
< 0.1%
ValueCountFrequency (%)
1030.834267 1
< 0.1%
989.787333 1
< 0.1%
985.411772 1
< 0.1%
976.7737 1
< 0.1%
976.00677 1
< 0.1%
974.554062 1
< 0.1%
970.515507 1
< 0.1%
963.167723 1
< 0.1%
960.595143 1
< 0.1%
955.164627 1
< 0.1%

폐기물에너지
Real number (ℝ)

ZEROS 

Distinct7565
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean776.39426
Minimum0
Maximum1609.8061
Zeros2436
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:48.198815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1487.77204
median985.83451
Q31175.5676
95-th percentile1306.572
Maximum1609.8061
Range1609.8061
Interquartile range (IQR)687.79552

Descriptive statistics

Standard deviation486.68636
Coefficient of variation (CV)0.62685466
Kurtosis-1.0939203
Mean776.39426
Median Absolute Deviation (MAD)261.82623
Skewness-0.66332073
Sum7763942.6
Variance236863.61
MonotonicityNot monotonic
2023-12-12T22:41:48.394006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2436
 
24.4%
1026.916543 1
 
< 0.1%
1070.22534 1
 
< 0.1%
1161.788756 1
 
< 0.1%
518.154423 1
 
< 0.1%
1002.214362 1
 
< 0.1%
1317.99683 1
 
< 0.1%
1114.79349 1
 
< 0.1%
922.79353 1
 
< 0.1%
739.768341 1
 
< 0.1%
Other values (7555) 7555
75.5%
ValueCountFrequency (%)
0.0 2436
24.4%
281.469586 1
 
< 0.1%
292.863648 1
 
< 0.1%
299.689668 1
 
< 0.1%
313.459647 1
 
< 0.1%
347.793315 1
 
< 0.1%
348.439295 1
 
< 0.1%
355.045577 1
 
< 0.1%
356.298349 1
 
< 0.1%
363.082743 1
 
< 0.1%
ValueCountFrequency (%)
1609.806113 1
< 0.1%
1581.335431 1
< 0.1%
1570.000132 1
< 0.1%
1566.126019 1
< 0.1%
1558.999265 1
< 0.1%
1551.973294 1
< 0.1%
1550.189447 1
< 0.1%
1549.824401 1
< 0.1%
1546.496666 1
< 0.1%
1543.771715 1
< 0.1%

신재생 합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2319.3968
Minimum750.31592
Maximum6676.4033
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:48.555282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum750.31592
5-th percentile1207.8058
Q11715.9193
median2168.1709
Q32779.2978
95-th percentile3914.8109
Maximum6676.4033
Range5926.0874
Interquartile range (IQR)1063.3786

Descriptive statistics

Standard deviation828.2016
Coefficient of variation (CV)0.3570763
Kurtosis0.76222042
Mean2319.3968
Median Absolute Deviation (MAD)507.87224
Skewness0.88462509
Sum23193968
Variance685917.9
MonotonicityNot monotonic
2023-12-12T22:41:48.728733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1632.610403 1
 
< 0.1%
1374.825323 1
 
< 0.1%
3877.864603 1
 
< 0.1%
4124.070556 1
 
< 0.1%
3585.083991 1
 
< 0.1%
1896.452647 1
 
< 0.1%
1699.891174 1
 
< 0.1%
1870.321357 1
 
< 0.1%
2727.003078 1
 
< 0.1%
3945.277147 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
750.315925 1
< 0.1%
809.121695 1
< 0.1%
813.090457 1
< 0.1%
818.502037 1
< 0.1%
825.949278 1
< 0.1%
832.514869 1
< 0.1%
840.443484 1
< 0.1%
845.30361 1
< 0.1%
849.689373 1
< 0.1%
858.255156 1
< 0.1%
ValueCountFrequency (%)
6676.403323 1
< 0.1%
6144.873107 1
< 0.1%
6114.326706 1
< 0.1%
6084.738512 1
< 0.1%
6049.066053 1
< 0.1%
5967.588978 1
< 0.1%
5965.260789 1
< 0.1%
5927.333953 1
< 0.1%
5839.758939 1
< 0.1%
5800.478404 1
< 0.1%

Interactions

2023-12-12T22:41:43.810264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:33.229043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:34.521985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.772990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.810280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.918137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.235430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.328108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.399655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:42.577278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:43.929281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:33.407890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:34.637714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.867450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.936536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:38.014711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.333594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.431835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.502557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:42.712025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:44.083685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:33.524718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:34.767779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.982219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.050922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:38.121562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.451826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.542026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.593793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:42.845769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:44.200397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:33.641457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:34.885105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.080447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.186161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:38.529612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.561980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.647050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.698040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:42.964267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:44.312135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:33.768466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.010582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.172699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.290567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:38.622547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.648726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.746058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.813854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:43.077525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:44.417970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:33.895913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.152593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.262277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.385962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:38.721449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.739333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.858092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.941839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:43.190187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:44.567957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:34.026526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.292340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.369924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.506468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:38.814466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.837054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.973458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:42.060022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:43.326867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:44.679000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:34.149264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.417004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.516234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.615042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:38.930756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.944909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.097926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:42.183420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:43.452133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:44.794055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:34.250646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.538781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.611501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.713568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.019114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.080173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.199810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:42.335325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:43.571377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:44.922762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:34.383399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:35.668005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:36.716551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:37.823061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:39.142861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:40.227608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:41.304084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:42.456069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:43.706142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:41:48.851516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거래시간연료전지석탄가스화복합화력(IGCC)태양광풍력수력해양에너지바이오에너지폐기물에너지신재생 합계
거래시간1.0000.0000.0210.5710.0950.5880.3830.0500.0750.503
연료전지0.0001.0000.7640.5020.5140.4990.0750.9100.8810.703
석탄가스화복합화력(IGCC)0.0210.7641.0000.3110.3940.2070.0320.6840.6150.465
태양광0.5710.5020.3111.0000.1280.3730.0990.4650.4350.881
풍력0.0950.5140.3940.1281.0000.1720.0530.4590.4290.432
수력0.5880.4990.2070.3730.1721.0000.0420.2750.3200.445
해양에너지0.3830.0750.0320.0990.0530.0421.0000.0600.0420.095
바이오에너지0.0500.9100.6840.4650.4590.2750.0601.0000.7900.643
폐기물에너지0.0750.8810.6150.4350.4290.3200.0420.7901.0000.641
신재생 합계0.5030.7030.4650.8810.4320.4450.0950.6430.6411.000
2023-12-12T22:41:49.042805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거래시간연료전지석탄가스화복합화력(IGCC)태양광풍력수력해양에너지바이오에너지폐기물에너지신재생 합계
거래시간1.000-0.0000.0070.240-0.0070.338-0.0060.004-0.0080.176
연료전지-0.0001.0000.6570.4230.426-0.0500.0080.948-0.2650.642
석탄가스화복합화력(IGCC)0.0070.6571.0000.2760.330-0.020-0.0010.659-0.3430.445
태양광0.2400.4230.2761.0000.0710.440-0.0400.415-0.1090.718
풍력-0.0070.4260.3300.0711.000-0.1360.0090.384-0.1230.375
수력0.338-0.050-0.0200.440-0.1361.000-0.047-0.018-0.1110.378
해양에너지-0.0060.008-0.001-0.0400.009-0.0471.0000.009-0.0100.056
바이오에너지0.0040.9480.6590.4150.384-0.0180.0091.000-0.2800.633
폐기물에너지-0.008-0.265-0.343-0.109-0.123-0.111-0.010-0.2801.0000.155
신재생 합계0.1760.6420.4450.7180.3750.3780.0560.6330.1551.000

Missing values

2023-12-12T22:41:45.370802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:41:45.531223image/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

거래일거래시간연료전지석탄가스화복합화력(IGCC)태양광풍력수력해양에너지바이오에너지폐기물에너지신재생 합계
228472014-08-092488.3128050.00.0251.675468171.9011370.072.1993531048.521641632.610403
760112020-09-024440.080468188.63040.195732291.8926391403.779296221.202072534.9600340.03080.740641
341552015-11-244125.0932380.00.00024244.71269382.87835162.071616161.8598381060.5526121837.168587
682872019-10-168273.3892350.0207.64745694.865836117.295098129.683568513.1725310.01336.053724
648622019-05-2615244.19307229.350241320.166753467.85348466.0516930.0250.2307761203.4705354181.316547
884142022-01-3123596.795806244.39296252.742309540.98410786.9468170.0811.2412260.02533.103225
325192015-09-1624120.1983790.00.016458142.29061272.494460.0152.37511276.3990631763.774072
369712016-03-2012130.8643310.0790.41737948.467133426.9293730.0161.3240221072.8611712630.863409
16192012-03-081245.211270.0192.19128222.563252668.0804270.052.1943610.2479631590.488494
233482014-08-3021111.3931790.00.02035233.454176303.855146180.77791266.3021341216.1695591911.972458
거래일거래시간연료전지석탄가스화복합화력(IGCC)태양광풍력수력해양에너지바이오에너지폐기물에너지신재생 합계
641142019-04-2511253.8177690.0441.098604353.517351597.76539498.388192474.8923771363.9335383583.413225
558852018-05-1714188.742580.0432.348774311.88746742.6118040.0138.160118839.3933372653.144073
337112015-11-0516128.311210.0249.71401334.27812270.5353250.0166.0106211194.6110491843.46034
536952018-02-158178.908899237.060889.749949249.942401347.5185278.072904276.5359151162.5034572470.292932
510842017-10-2913170.5302720.0965.512238524.099199112.7199080.0356.6551281070.6562563200.173001
676152019-09-188274.70522233.1784344.307637218.629783212.128588210.728952497.653772753.6409672744.973319
627522019-02-2717255.2916270.0273.72091984.637306589.7733530.0245.7672751133.0075262582.198006
426212016-11-1022128.050194232.244320.0150.23524217.8086240.0219.4221971046.959471994.720045
394282016-06-3021128.4260410.00.00016269.969356680.0164370.0184.3715341034.6224762297.406004
634892019-03-3010257.895150.0987.179877424.047286366.3710230.0455.0757651524.4777854015.046886