Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory947.3 KiB
Average record size in memory97.0 B

Variable types

DateTime1
Numeric9

Dataset

Description한국동서발전에서 제주지역 태양광 발전량 예측을 위해 1시간 주기 기상청의 종관기상관측, 전력거래소의 시장참여 태양광 설비 및 발전량 데이터를 연계하여 가공한 데이터 입니다.일시, 기온, 강수량, 일사량, 태양광 설비용량, 태양광 발전량 등의 항목으로 구성됩니다.
Author한국동서발전(주)
URLhttps://www.data.go.kr/data/15126430/fileData.do

Alerts

일조(hr) is highly overall correlated with 일사량 and 1 other fieldsHigh correlation
일사량 is highly overall correlated with 일조(hr) and 1 other fieldsHigh correlation
태양광 발전량(MWh) is highly overall correlated with 일조(hr) and 1 other fieldsHigh correlation
적설(cm) is highly skewed (γ1 = 21.47697729)Skewed
일시 has unique valuesUnique
강수량(mm) has 9365 (93.7%) zerosZeros
적설(cm) has 9914 (99.1%) zerosZeros
전운량(10분위) has 1415 (14.1%) zerosZeros
일조(hr) has 6824 (68.2%) zerosZeros
일사량 has 4986 (49.9%) zerosZeros
태양광 발전량(MWh) has 1135 (11.3%) zerosZeros

Reproduction

Analysis started2024-03-14 21:21:47.759974
Analysis finished2024-03-14 21:22:09.427723
Duration21.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일시
Date

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-01-01 01:00:00
Maximum2022-12-31 21:00:00
2024-03-15T06:22:09.570085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:09.809005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

기온
Real number (ℝ)

Distinct363
Distinct (%)3.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.986627
Minimum-2.7
Maximum36.2
Zeros0
Zeros (%)0.0%
Negative14
Negative (%)0.1%
Memory size166.0 KiB
2024-03-15T06:22:10.132979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.7
5-th percentile4.285
Q110.5
median17.1
Q323.5
95-th percentile29.5
Maximum36.2
Range38.9
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.0153559
Coefficient of variation (CV)0.4718627
Kurtosis-0.98513155
Mean16.986627
Median Absolute Deviation (MAD)6.5
Skewness-0.0073865721
Sum169832.3
Variance64.24593
MonotonicityNot monotonic
2024-03-15T06:22:10.589877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.7 58
 
0.6%
12.2 57
 
0.6%
19.4 54
 
0.5%
27.0 52
 
0.5%
11.6 52
 
0.5%
11.7 51
 
0.5%
20.5 51
 
0.5%
23.5 50
 
0.5%
22.6 50
 
0.5%
19.1 50
 
0.5%
Other values (353) 9473
94.7%
ValueCountFrequency (%)
-2.7 1
< 0.1%
-1.9 2
< 0.1%
-1.5 1
< 0.1%
-1.4 1
< 0.1%
-1.1 2
< 0.1%
-1.0 1
< 0.1%
-0.8 1
< 0.1%
-0.6 2
< 0.1%
-0.5 1
< 0.1%
-0.3 2
< 0.1%
ValueCountFrequency (%)
36.2 1
 
< 0.1%
35.7 1
 
< 0.1%
35.6 1
 
< 0.1%
35.5 1
 
< 0.1%
35.2 2
< 0.1%
35.1 3
< 0.1%
34.9 4
< 0.1%
34.8 2
< 0.1%
34.7 1
 
< 0.1%
34.6 2
< 0.1%

강수량(mm)
Real number (ℝ)

ZEROS 

Distinct104
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14466
Minimum0
Maximum37
Zeros9365
Zeros (%)93.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:22:11.122683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0950632
Coefficient of variation (CV)7.5699101
Kurtosis317.46389
Mean0.14466
Median Absolute Deviation (MAD)0
Skewness14.926148
Sum1446.6
Variance1.1991634
MonotonicityNot monotonic
2024-03-15T06:22:11.594446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9365
93.7%
0.1 93
 
0.9%
0.2 57
 
0.6%
0.3 34
 
0.3%
0.5 33
 
0.3%
0.7 29
 
0.3%
0.4 27
 
0.3%
0.8 26
 
0.3%
1.0 20
 
0.2%
0.9 17
 
0.2%
Other values (94) 299
 
3.0%
ValueCountFrequency (%)
0.0 9365
93.7%
0.1 93
 
0.9%
0.2 57
 
0.6%
0.3 34
 
0.3%
0.4 27
 
0.3%
0.5 33
 
0.3%
0.6 12
 
0.1%
0.7 29
 
0.3%
0.8 26
 
0.3%
0.9 17
 
0.2%
ValueCountFrequency (%)
37.0 1
< 0.1%
30.7 1
< 0.1%
24.2 1
< 0.1%
22.9 1
< 0.1%
21.7 1
< 0.1%
20.9 1
< 0.1%
20.5 1
< 0.1%
17.1 1
< 0.1%
15.3 1
< 0.1%
15.1 1
< 0.1%

습도
Real number (ℝ)

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.0419
Minimum14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:22:12.226055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile46
Q160
median70
Q381
95-th percentile94
Maximum100
Range86
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.767408
Coefficient of variation (CV)0.21083678
Kurtosis-0.3280668
Mean70.0419
Median Absolute Deviation (MAD)11
Skewness-0.16567766
Sum700419
Variance218.07635
MonotonicityNot monotonic
2024-03-15T06:22:12.710648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 289
 
2.9%
64 274
 
2.7%
73 262
 
2.6%
67 262
 
2.6%
66 257
 
2.6%
70 247
 
2.5%
65 244
 
2.4%
76 242
 
2.4%
63 241
 
2.4%
71 238
 
2.4%
Other values (73) 7444
74.4%
ValueCountFrequency (%)
14 2
 
< 0.1%
18 1
 
< 0.1%
20 1
 
< 0.1%
21 2
 
< 0.1%
22 1
 
< 0.1%
23 4
 
< 0.1%
24 5
0.1%
25 10
0.1%
26 4
 
< 0.1%
27 7
0.1%
ValueCountFrequency (%)
100 36
 
0.4%
99 51
 
0.5%
98 65
0.7%
97 91
0.9%
96 101
1.0%
95 114
1.1%
94 127
1.3%
93 141
1.4%
92 120
1.2%
91 126
1.3%

적설(cm)
Real number (ℝ)

SKEWED  ZEROS 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01841
Minimum0
Maximum9.2
Zeros9914
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:22:13.277724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9.2
Range9.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29797139
Coefficient of variation (CV)16.185301
Kurtosis515.53282
Mean0.01841
Median Absolute Deviation (MAD)0
Skewness21.476977
Sum184.1
Variance0.088786951
MonotonicityNot monotonic
2024-03-15T06:22:13.786261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 9914
99.1%
0.5 12
 
0.1%
1.0 6
 
0.1%
0.4 5
 
0.1%
0.7 5
 
0.1%
0.3 5
 
0.1%
0.2 4
 
< 0.1%
5.0 4
 
< 0.1%
0.1 3
 
< 0.1%
1.2 3
 
< 0.1%
Other values (25) 39
 
0.4%
ValueCountFrequency (%)
0.0 9914
99.1%
0.1 3
 
< 0.1%
0.2 4
 
< 0.1%
0.3 5
 
0.1%
0.4 5
 
0.1%
0.5 12
 
0.1%
0.6 1
 
< 0.1%
0.7 5
 
0.1%
0.8 3
 
< 0.1%
0.9 1
 
< 0.1%
ValueCountFrequency (%)
9.2 1
 
< 0.1%
8.8 2
< 0.1%
8.5 1
 
< 0.1%
7.7 1
 
< 0.1%
7.0 1
 
< 0.1%
6.5 1
 
< 0.1%
6.1 1
 
< 0.1%
5.6 1
 
< 0.1%
5.4 1
 
< 0.1%
5.0 4
< 0.1%

전운량(10분위)
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1659
Minimum0
Maximum10
Zeros1415
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:22:14.351577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6132925
Coefficient of variation (CV)0.58601218
Kurtosis-1.1584372
Mean6.1659
Median Absolute Deviation (MAD)3
Skewness-0.55771438
Sum61659
Variance13.055883
MonotonicityNot monotonic
2024-03-15T06:22:14.811335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 2423
24.2%
0 1415
14.1%
9 1393
13.9%
8 1030
10.3%
7 853
 
8.5%
6 598
 
6.0%
3 498
 
5.0%
5 490
 
4.9%
2 463
 
4.6%
4 462
 
4.6%
ValueCountFrequency (%)
0 1415
14.1%
1 375
 
3.8%
2 463
 
4.6%
3 498
 
5.0%
4 462
 
4.6%
5 490
 
4.9%
6 598
6.0%
7 853
8.5%
8 1030
10.3%
9 1393
13.9%
ValueCountFrequency (%)
10 2423
24.2%
9 1393
13.9%
8 1030
10.3%
7 853
 
8.5%
6 598
 
6.0%
5 490
 
4.9%
4 462
 
4.6%
3 498
 
5.0%
2 463
 
4.6%
1 375
 
3.8%

일조(hr)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22393
Minimum0
Maximum1
Zeros6824
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:22:15.371189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.37760113
Coefficient of variation (CV)1.6862463
Kurtosis-0.061978611
Mean0.22393
Median Absolute Deviation (MAD)0
Skewness1.3084025
Sum2239.3
Variance0.14258261
MonotonicityNot monotonic
2024-03-15T06:22:15.770061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 6824
68.2%
1.0 1355
 
13.6%
0.1 308
 
3.1%
0.9 263
 
2.6%
0.3 198
 
2.0%
0.2 190
 
1.9%
0.7 186
 
1.9%
0.8 178
 
1.8%
0.5 174
 
1.7%
0.4 173
 
1.7%
ValueCountFrequency (%)
0.0 6824
68.2%
0.1 308
 
3.1%
0.2 190
 
1.9%
0.3 198
 
2.0%
0.4 173
 
1.7%
0.5 174
 
1.7%
0.6 151
 
1.5%
0.7 186
 
1.9%
0.8 178
 
1.8%
0.9 263
 
2.6%
ValueCountFrequency (%)
1.0 1355
13.6%
0.9 263
 
2.6%
0.8 178
 
1.8%
0.7 186
 
1.9%
0.6 151
 
1.5%
0.5 174
 
1.7%
0.4 173
 
1.7%
0.3 198
 
2.0%
0.2 190
 
1.9%
0.1 308
 
3.1%

일사량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct365
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.585019
Minimum0
Maximum3.7
Zeros4986
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:22:16.166405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.88
95-th percentile2.78
Maximum3.7
Range3.7
Interquartile range (IQR)0.88

Descriptive statistics

Standard deviation0.92414795
Coefficient of variation (CV)1.5796888
Kurtosis1.3015921
Mean0.585019
Median Absolute Deviation (MAD)0.01
Skewness1.5728849
Sum5850.19
Variance0.85404942
MonotonicityNot monotonic
2024-03-15T06:22:16.750172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4986
49.9%
0.01 139
 
1.4%
0.02 97
 
1.0%
0.03 94
 
0.9%
0.04 67
 
0.7%
0.09 53
 
0.5%
0.05 52
 
0.5%
0.06 51
 
0.5%
0.07 49
 
0.5%
0.12 47
 
0.5%
Other values (355) 4365
43.6%
ValueCountFrequency (%)
0.0 4986
49.9%
0.01 139
 
1.4%
0.02 97
 
1.0%
0.03 94
 
0.9%
0.04 67
 
0.7%
0.05 52
 
0.5%
0.06 51
 
0.5%
0.07 49
 
0.5%
0.08 41
 
0.4%
0.09 53
 
0.5%
ValueCountFrequency (%)
3.7 1
 
< 0.1%
3.69 1
 
< 0.1%
3.65 1
 
< 0.1%
3.63 3
< 0.1%
3.61 1
 
< 0.1%
3.6 2
< 0.1%
3.59 1
 
< 0.1%
3.58 1
 
< 0.1%
3.57 2
< 0.1%
3.55 1
 
< 0.1%
Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.04081
Minimum263.225
Maximum368.121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:22:17.178165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum263.225
5-th percentile266.195
Q1284.907
median297.708
Q3312.984
95-th percentile362.465
Maximum368.121
Range104.896
Interquartile range (IQR)28.077

Descriptive statistics

Standard deviation24.390247
Coefficient of variation (CV)0.080751494
Kurtosis0.86677368
Mean302.04081
Median Absolute Deviation (MAD)13.22
Skewness0.96592038
Sum3020408.1
Variance594.88413
MonotonicityNot monotonic
2024-03-15T06:22:17.605203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
318.531 306
 
3.1%
269.815 305
 
3.0%
289.459 303
 
3.0%
308.959 301
 
3.0%
288.53 297
 
3.0%
263.225 295
 
2.9%
278.569 294
 
2.9%
289.532 294
 
2.9%
302.092 292
 
2.9%
318.119 283
 
2.8%
Other values (26) 7030
70.3%
ValueCountFrequency (%)
263.225 295
2.9%
266.195 248
2.5%
269.815 305
3.0%
272.061 267
2.7%
277.87 274
2.7%
278.569 294
2.9%
282.355 281
2.8%
284.069 279
2.8%
284.907 281
2.8%
288.53 297
3.0%
ValueCountFrequency (%)
368.121 281
2.8%
362.465 270
2.7%
354.942 282
2.8%
338.319 269
2.7%
323.159 258
2.6%
318.531 306
3.1%
318.119 283
2.8%
314.758 261
2.6%
313.437 268
2.7%
312.984 279
2.8%

태양광 발전량(MWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4741
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.682091
Minimum0
Maximum257.39
Zeros1135
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T06:22:18.291800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median6.32
Q367.8925
95-th percentile185.575
Maximum257.39
Range257.39
Interquartile range (IQR)67.6925

Descriptive statistics

Standard deviation63.080578
Coefficient of variation (CV)1.4779168
Kurtosis0.90663736
Mean42.682091
Median Absolute Deviation (MAD)6.32
Skewness1.4575685
Sum426820.91
Variance3979.1594
MonotonicityNot monotonic
2024-03-15T06:22:18.652225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1135
 
11.3%
0.01 181
 
1.8%
0.06 153
 
1.5%
0.19 150
 
1.5%
0.25 128
 
1.3%
0.02 116
 
1.2%
0.05 99
 
1.0%
0.18 93
 
0.9%
0.03 82
 
0.8%
0.24 74
 
0.7%
Other values (4731) 7789
77.9%
ValueCountFrequency (%)
0.0 1135
11.3%
0.01 181
 
1.8%
0.02 116
 
1.2%
0.03 82
 
0.8%
0.04 52
 
0.5%
0.05 99
 
1.0%
0.06 153
 
1.5%
0.07 42
 
0.4%
0.08 38
 
0.4%
0.09 35
 
0.4%
ValueCountFrequency (%)
257.39 1
< 0.1%
257.35 1
< 0.1%
257.19 1
< 0.1%
255.55 1
< 0.1%
255.31 1
< 0.1%
254.13 1
< 0.1%
254.04 1
< 0.1%
252.22 1
< 0.1%
250.89 1
< 0.1%
247.56 1
< 0.1%

Interactions

2024-03-15T06:22:06.645521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:48.673891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:51.366198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:54.309369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:56.037610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:58.210992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:59.868058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:01.768659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:04.030682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:06.923703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:48.956076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:51.715130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:54.479983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:56.258619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:58.459209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:00.150360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:02.088818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:04.311804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:07.202536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:49.243730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:52.089991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:54.646289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:56.520708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:58.633065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:00.337795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:02.356029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:04.587710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:07.462931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:49.505527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:52.362128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:54.797309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:56.771160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:58.858090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:00.497623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:02.622827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:04.844514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:07.785544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:49.762858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:52.635927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:54.961975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:57.019364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:59.039825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:00.652908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:02.820043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:05.104844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:08.057215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:50.033280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:52.980324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:55.122444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:57.270977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:59.207107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:00.814322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:03.011626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:05.370886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:08.280854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:50.303241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:53.267561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:55.380344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:57.602193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:59.371763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:00.991976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:03.192834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:05.644958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:08.483031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:50.768680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:53.743202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:55.650231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:57.877867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:59.544950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:01.241277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:03.480533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:06.112198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:08.648722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:51.084623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:54.052961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:55.858120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:58.038485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:21:59.709102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:01.508223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:03.754528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:22:06.378651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:22:18.874004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온강수량(mm)습도적설(cm)전운량(10분위)일조(hr)일사량태양광 설비용량(MW)태양광 발전량(MWh)
기온1.0000.0430.5050.5010.2970.3460.3920.5240.290
강수량(mm)0.0431.0000.1970.0000.1280.0000.0000.0350.000
습도0.5050.1971.0000.0000.4790.4100.3650.2580.375
적설(cm)0.5010.0000.0001.0000.0680.0000.0000.0780.000
전운량(10분위)0.2970.1280.4790.0681.0000.5180.3490.1720.379
일조(hr)0.3460.0000.4100.0000.5181.0000.7490.0950.684
일사량0.3920.0000.3650.0000.3490.7491.0000.1000.777
태양광 설비용량(MW)0.5240.0350.2580.0780.1720.0950.1001.0000.116
태양광 발전량(MWh)0.2900.0000.3750.0000.3790.6840.7770.1161.000
2024-03-15T06:22:19.115269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온강수량(mm)습도적설(cm)전운량(10분위)일조(hr)일사량태양광 설비용량(MW)태양광 발전량(MWh)
기온1.0000.0730.345-0.1580.0020.2430.2990.1360.222
강수량(mm)0.0731.0000.3410.0890.319-0.157-0.086-0.003-0.100
습도0.3450.3411.0000.0140.382-0.365-0.2690.089-0.277
적설(cm)-0.1580.0890.0141.0000.045-0.054-0.0430.009-0.065
전운량(10분위)0.0020.3190.3820.0451.000-0.431-0.171-0.019-0.201
일조(hr)0.243-0.157-0.365-0.054-0.4311.0000.8250.0190.719
일사량0.299-0.086-0.269-0.043-0.1710.8251.000-0.0150.889
태양광 설비용량(MW)0.136-0.0030.0890.009-0.0190.019-0.0151.0000.052
태양광 발전량(MWh)0.222-0.100-0.277-0.065-0.2010.7190.8890.0521.000

Missing values

2024-03-15T06:22:08.866293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:22:09.241951image/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

일시기온강수량(mm)습도적설(cm)전운량(10분위)일조(hr)일사량태양광 설비용량(MW)태양광 발전량(MWh)
236792022-09-14 15:0025.10.0920.0100.00.15338.31975.07
86582020-12-27 18:0011.30.0900.0100.00.01292.5551.24
216392022-06-21 15:0027.10.0750.020.92.79314.758158.51
69472020-10-17 11:0019.90.0430.001.02.22295.678199.83
148152021-09-10 07:0021.81.6930.0100.00.0303.6852.17
14472020-03-02 07:007.80.0640.080.00.0269.8159.44
14972020-03-04 09:009.30.0670.090.00.26269.81560.15
37102020-06-04 14:0022.30.0760.090.82.3284.907144.32
146442021-09-03 04:0023.40.0990.0100.00.0303.6850.06
19392020-03-22 19:0012.90.0630.030.10.09269.8157.48
일시기온강수량(mm)습도적설(cm)전운량(10분위)일조(hr)일사량태양광 설비용량(MW)태양광 발전량(MWh)
251042022-11-13 00:0019.60.0920.070.00.0362.4650.0
79942020-11-30 02:008.40.0720.0100.00.0289.5320.0
200292022-04-15 13:0016.30.0650.051.03.33308.874203.89
192802022-03-15 08:007.50.0880.010.50.25308.95996.82
127162021-06-14 20:0021.30.0900.060.00.06299.6977.67
92692021-01-22 05:0013.10.0950.0100.00.0289.4590.03
234002022-09-03 00:0023.71.4980.0100.00.0338.3190.0
116262021-04-30 10:0018.30.0580.001.02.51296.447209.65
142522021-08-17 20:0025.60.0700.020.00.01302.0924.74
54442020-08-15 20:0030.20.0710.030.10.02278.5695.09