Overview

Dataset statistics

Number of variables9
Number of observations969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.8 KiB
Average record size in memory80.1 B

Variable types

Categorical2
Numeric7

Dataset

Description태양광, 풍력 발전기에 연계되어 있는 전기저장장치(ESS)의 월별 지역별 설비용량(PCS*용량) 자료입니다. 값은 변동될 수 있으며, 전력시장에 참여하는 발전기만을 대상으로 합니다.(PPA, 자가용 제외) ○ 기간 : 2018년 1월 ~ 2022년 09월 ○ 항목 : 연도, 월, 지역(광역시도), 태양광 전체 설비용량, 풍력 전체 설비용량, 전기저장장치 연계 태양광 설비용량, 전기저장장치 연계 풍력 설비용량, 태양광연계 전기저장장치 설비용량, 풍력연계 전기저장장치 설비용량 ○ 단위 : MW * ESS : Electric power Storage System, 전기저장장치 ＊ PCS : Power Conversion System, 전력변환장치
URLhttps://www.data.go.kr/data/15080672/fileData.do

Alerts

태양광 전체 설비용량 is highly overall correlated with 풍력 전체 설비용량 and 4 other fieldsHigh correlation
풍력 전체 설비용량 is highly overall correlated with 태양광 전체 설비용량 and 5 other fieldsHigh correlation
전기저장장치 연계 태양광 설비용량 is highly overall correlated with 태양광 전체 설비용량 and 4 other fieldsHigh correlation
전기저장장치 연계 풍력 설비용량 is highly overall correlated with 태양광 전체 설비용량 and 5 other fieldsHigh correlation
태양광 연계 전기저장장치 설비용량 is highly overall correlated with 태양광 전체 설비용량 and 4 other fieldsHigh correlation
풍력 연계 전기저장장치 설비용량 is highly overall correlated with 태양광 전체 설비용량 and 5 other fieldsHigh correlation
지역 is highly overall correlated with 풍력 전체 설비용량 and 2 other fieldsHigh correlation
풍력 전체 설비용량 has 342 (35.3%) zerosZeros
전기저장장치 연계 태양광 설비용량 has 74 (7.6%) zerosZeros
전기저장장치 연계 풍력 설비용량 has 513 (52.9%) zerosZeros
태양광 연계 전기저장장치 설비용량 has 50 (5.2%) zerosZeros
풍력 연계 전기저장장치 설비용량 has 513 (52.9%) zerosZeros

Reproduction

Analysis started2023-12-12 14:04:18.572388
Analysis finished2023-12-12 14:04:25.183723
Duration6.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2018
204 
2019
204 
2020
204 
2021
204 
2022
153 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 204
21.1%
2019 204
21.1%
2020 204
21.1%
2021 204
21.1%
2022 153
15.8%

Length

2023-12-12T23:04:25.251383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:04:25.361021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 204
21.1%
2019 204
21.1%
2020 204
21.1%
2021 204
21.1%
2022 153
15.8%

월
Real number (ℝ)

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2631579
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T23:04:25.483398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3761889
Coefficient of variation (CV)0.53905537
Kurtosis-1.1524304
Mean6.2631579
Median Absolute Deviation (MAD)3
Skewness0.07387559
Sum6069
Variance11.398652
MonotonicityNot monotonic
2023-12-12T23:04:25.621024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 85
8.8%
2 85
8.8%
3 85
8.8%
4 85
8.8%
5 85
8.8%
6 85
8.8%
7 85
8.8%
8 85
8.8%
9 85
8.8%
10 68
7.0%
Other values (2) 136
14.0%
ValueCountFrequency (%)
1 85
8.8%
2 85
8.8%
3 85
8.8%
4 85
8.8%
5 85
8.8%
6 85
8.8%
7 85
8.8%
8 85
8.8%
9 85
8.8%
10 68
7.0%
ValueCountFrequency (%)
12 68
7.0%
11 68
7.0%
10 68
7.0%
9 85
8.8%
8 85
8.8%
7 85
8.8%
6 85
8.8%
5 85
8.8%
4 85
8.8%
3 85
8.8%

지역
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
경기도
 
57
강원도
 
57
경상남도
 
57
경상북도
 
57
전라남도
 
57
Other values (12)
684 

Length

Max length7
Median length5
Mean length4.4117647
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row강원도
3rd row경상남도
4th row경상북도
5th row전라남도

Common Values

ValueCountFrequency (%)
경기도 57
 
5.9%
강원도 57
 
5.9%
경상남도 57
 
5.9%
경상북도 57
 
5.9%
전라남도 57
 
5.9%
전라북도 57
 
5.9%
충청남도 57
 
5.9%
충청북도 57
 
5.9%
제주도 57
 
5.9%
서울특별시 57
 
5.9%
Other values (7) 399
41.2%

Length

2023-12-12T23:04:25.786407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 57
 
5.9%
서울특별시 57
 
5.9%
울산광역시 57
 
5.9%
세종특별자치시 57
 
5.9%
대구광역시 57
 
5.9%
광주광역시 57
 
5.9%
대전광역시 57
 
5.9%
인천광역시 57
 
5.9%
제주도 57
 
5.9%
강원도 57
 
5.9%
Other values (7) 399
41.2%

태양광 전체 설비용량
Real number (ℝ)

HIGH CORRELATION 

Distinct613
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.6084
Minimum9
Maximum1985.4564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T23:04:25.968385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile11.6
Q130.6
median128
Q3309
95-th percentile746.7
Maximum1985.4564
Range1976.4564
Interquartile range (IQR)278.4

Descriptive statistics

Standard deviation325.05835
Coefficient of variation (CV)1.3623089
Kurtosis9.9903115
Mean238.6084
Median Absolute Deviation (MAD)113.6036
Skewness2.8282597
Sum231211.54
Variance105662.93
MonotonicityNot monotonic
2023-12-12T23:04:26.156542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.6 29
 
3.0%
12.7 17
 
1.8%
14.7 17
 
1.8%
36.6 16
 
1.7%
20.7 15
 
1.5%
56.6 14
 
1.4%
29.6 13
 
1.3%
25.6 11
 
1.1%
12.1 11
 
1.1%
11.4 9
 
0.9%
Other values (603) 817
84.3%
ValueCountFrequency (%)
9.0 1
 
0.1%
10.5 4
 
0.4%
11.2 4
 
0.4%
11.3 5
 
0.5%
11.4 9
 
0.9%
11.6 29
3.0%
12.1 11
 
1.1%
12.2 7
 
0.7%
12.5 8
 
0.8%
12.6 4
 
0.4%
ValueCountFrequency (%)
1985.456395 1
0.1%
1979.60705 1
0.1%
1939.894505 1
0.1%
1927.990675 1
0.1%
1922.230275 1
0.1%
1898.240855 1
0.1%
1891.569645 1
0.1%
1863.737775 1
0.1%
1851.083335 1
0.1%
1824.86876 1
0.1%

풍력 전체 설비용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.91972
Minimum0
Maximum532.108
Zeros342
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T23:04:26.658756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q379.5
95-th percentile421.16
Maximum532.108
Range532.108
Interquartile range (IQR)79.5

Descriptive statistics

Standard deviation146.8011
Coefficient of variation (CV)1.5970577
Kurtosis0.16958836
Mean91.91972
Median Absolute Deviation (MAD)2
Skewness1.346777
Sum89070.209
Variance21550.563
MonotonicityNot monotonic
2023-12-12T23:04:26.836277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.0 342
35.3%
2.0 57
 
5.9%
49.0 57
 
5.9%
5.3 44
 
4.5%
1.7 44
 
4.5%
0.8 44
 
4.5%
48.6 40
 
4.1%
79.5 39
 
4.0%
294.6 27
 
2.8%
328.0 24
 
2.5%
Other values (38) 251
25.9%
ValueCountFrequency (%)
0.0 342
35.3%
0.75 13
 
1.3%
0.8 44
 
4.5%
1.65 13
 
1.3%
1.7 44
 
4.5%
2.0 57
 
5.9%
5.25 13
 
1.3%
5.3 44
 
4.5%
19.5 18
 
1.9%
47.8 4
 
0.4%
ValueCountFrequency (%)
532.108 2
 
0.2%
480.468 1
 
0.1%
461.5 8
0.8%
459.468 10
1.0%
450.7 1
 
0.1%
439.9 1
 
0.1%
421.2 15
1.5%
421.16 13
1.3%
409.6 9
0.9%
390.9 2
 
0.2%

전기저장장치 연계 태양광 설비용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct291
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.036204
Minimum0
Maximum908.3
Zeros74
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T23:04:27.012094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.97008
median11.8
Q383.1
95-th percentile257.2
Maximum908.3
Range908.3
Interquartile range (IQR)79.12992

Descriptive statistics

Standard deviation157.05247
Coefficient of variation (CV)2.1503373
Kurtosis18.241943
Mean73.036204
Median Absolute Deviation (MAD)11.6
Skewness4.1209141
Sum70772.082
Variance24665.479
MonotonicityNot monotonic
2023-12-12T23:04:27.187874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 74
 
7.6%
0.3 35
 
3.6%
3.5 21
 
2.2%
6.5 21
 
2.2%
10.3 19
 
2.0%
4.2 19
 
2.0%
6.9 19
 
2.0%
7.2 18
 
1.9%
2.0 14
 
1.4%
99.949985 13
 
1.3%
Other values (281) 716
73.9%
ValueCountFrequency (%)
0.0 74
7.6%
0.1 4
 
0.4%
0.2 5
 
0.5%
0.29512 13
 
1.3%
0.3 35
3.6%
0.5 11
 
1.1%
0.6 12
 
1.2%
1.0 4
 
0.4%
1.1 2
 
0.2%
1.6 12
 
1.2%
ValueCountFrequency (%)
908.3 9
0.9%
904.87691 7
0.7%
904.77771 6
0.6%
874.1 1
 
0.1%
777.9 1
 
0.1%
776.8 1
 
0.1%
776.7 3
 
0.3%
563.8 1
 
0.1%
528.7 1
 
0.1%
410.4 1
 
0.1%

전기저장장치 연계 풍력 설비용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.162436
Minimum0
Maximum135.8
Zeros513
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T23:04:27.338222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q346
95-th percentile135.8
Maximum135.8
Range135.8
Interquartile range (IQR)46

Descriptive statistics

Standard deviation44.077359
Coefficient of variation (CV)1.5651118
Kurtosis0.56103945
Mean28.162436
Median Absolute Deviation (MAD)0
Skewness1.4158002
Sum27289.4
Variance1942.8136
MonotonicityNot monotonic
2023-12-12T23:04:27.455424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 513
52.9%
46.0 70
 
7.2%
3.0 57
 
5.9%
135.8 52
 
5.4%
5.0 44
 
4.5%
48.0 44
 
4.5%
32.8 39
 
4.0%
107.0 39
 
4.0%
126.0 28
 
2.9%
114.5 24
 
2.5%
Other values (6) 59
 
6.1%
ValueCountFrequency (%)
0.0 513
52.9%
3.0 57
 
5.9%
4.95 13
 
1.3%
5.0 44
 
4.5%
14.0 5
 
0.5%
32.75 13
 
1.3%
32.8 39
 
4.0%
46.0 70
 
7.2%
48.0 44
 
4.5%
75.8 5
 
0.5%
ValueCountFrequency (%)
135.8 52
5.4%
126.0 28
 
2.9%
114.5 24
 
2.5%
107.0 39
4.0%
90.3 5
 
0.5%
77.0 18
 
1.9%
75.8 5
 
0.5%
48.0 44
4.5%
46.0 70
7.2%
32.8 39
4.0%

태양광 연계 전기저장장치 설비용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct294
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.844374
Minimum0
Maximum798.81156
Zeros50
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T23:04:27.594275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.3
median10.8
Q367.6
95-th percentile225.9
Maximum798.81156
Range798.81156
Interquartile range (IQR)64.3

Descriptive statistics

Standard deviation137.7318
Coefficient of variation (CV)2.1573052
Kurtosis18.518993
Mean63.844374
Median Absolute Deviation (MAD)10.55
Skewness4.1485292
Sum61865.198
Variance18970.049
MonotonicityNot monotonic
2023-12-12T23:04:27.754477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 50
 
5.2%
0.3 35
 
3.6%
6.0 32
 
3.3%
5.0 23
 
2.4%
3.3 21
 
2.2%
6.2 20
 
2.1%
8.4 18
 
1.9%
1.0 16
 
1.7%
6.6 15
 
1.5%
0.1 15
 
1.5%
Other values (284) 724
74.7%
ValueCountFrequency (%)
0.0 50
5.2%
0.08 13
 
1.3%
0.1 15
 
1.5%
0.2 5
 
0.5%
0.25 13
 
1.3%
0.3 35
3.6%
0.5 11
 
1.1%
0.6 12
 
1.2%
1.0 16
 
1.7%
1.1 2
 
0.2%
ValueCountFrequency (%)
798.81156 3
 
0.3%
798.73156 10
1.0%
798.7 9
0.9%
766.6 1
 
0.1%
674.4 1
 
0.1%
673.7 1
 
0.1%
673.6 3
 
0.3%
478.6 1
 
0.1%
446.9 1
 
0.1%
344.0 1
 
0.1%

풍력 연계 전기저장장치 설비용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5255418
Minimum0
Maximum35
Zeros513
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-12T23:04:27.907243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile29.8
Maximum35
Range35
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.2827319
Coefficient of variation (CV)1.6799677
Kurtosis2.0811851
Mean5.5255418
Median Absolute Deviation (MAD)0
Skewness1.7614925
Sum5354.25
Variance86.169112
MonotonicityNot monotonic
2023-12-12T23:04:28.029377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 513
52.9%
0.1 57
 
5.9%
9.0 57
 
5.9%
1.2 57
 
5.9%
8.0 57
 
5.9%
5.0 52
 
5.4%
20.3 39
 
4.0%
22.5 39
 
4.0%
35.0 28
 
2.9%
29.8 24
 
2.5%
Other values (5) 46
 
4.7%
ValueCountFrequency (%)
0.0 513
52.9%
0.1 57
 
5.9%
1.2 57
 
5.9%
3.0 5
 
0.5%
5.0 52
 
5.4%
8.0 57
 
5.9%
9.0 57
 
5.9%
9.3 5
 
0.5%
13.5 18
 
1.9%
20.25 13
 
1.3%
ValueCountFrequency (%)
35.0 28
2.9%
29.8 24
2.5%
23.8 5
 
0.5%
22.5 39
4.0%
20.3 39
4.0%
20.25 13
 
1.3%
13.5 18
 
1.9%
9.3 5
 
0.5%
9.0 57
5.9%
8.0 57
5.9%

Interactions

2023-12-12T23:04:23.843494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:19.088404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:20.209617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.002652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.742279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.439337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:23.129813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:24.004246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:19.211804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:20.327354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.094863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.883713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.528814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:23.227594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:24.152719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:19.317585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:20.451675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.196996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.986276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.626404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:23.331039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:24.270270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:19.761981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:20.566235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.290807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.077232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.723131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:23.430866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:24.420979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:19.873620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:20.667137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.383712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.160584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.829895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:23.524751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:24.590783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:19.985757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:20.776690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.497733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.251503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.927410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:23.627255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:24.769508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:20.100205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:20.900371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:21.608605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:22.341064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:23.029869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:23.734322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:04:28.128293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월지역태양광 전체 설비용량풍력 전체 설비용량전기저장장치 연계 태양광 설비용량전기저장장치 연계 풍력 설비용량태양광 연계 전기저장장치 설비용량풍력 연계 전기저장장치 설비용량
연도1.0000.1360.0000.4410.2170.2910.2690.2690.191
월0.1361.0000.0000.0000.0000.0000.0000.0000.000
지역0.0000.0001.0000.7610.8870.7700.9290.7740.953
태양광 전체 설비용량0.4410.0000.7611.0000.6560.9160.6140.9480.696
풍력 전체 설비용량0.2170.0000.8870.6561.0000.7680.9030.6160.811
전기저장장치 연계 태양광 설비용량0.2910.0000.7700.9160.7681.0000.6620.9830.626
전기저장장치 연계 풍력 설비용량0.2690.0000.9290.6140.9030.6621.0000.5150.969
태양광 연계 전기저장장치 설비용량0.2690.0000.7740.9480.6160.9830.5151.0000.780
풍력 연계 전기저장장치 설비용량0.1910.0000.9530.6960.8110.6260.9690.7801.000
2023-12-12T23:04:28.326146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역연도
지역1.0000.000
연도0.0001.000
2023-12-12T23:04:28.415013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월태양광 전체 설비용량풍력 전체 설비용량전기저장장치 연계 태양광 설비용량전기저장장치 연계 풍력 설비용량태양광 연계 전기저장장치 설비용량풍력 연계 전기저장장치 설비용량연도지역
월1.0000.0250.0060.0450.0050.0480.0030.0570.000
태양광 전체 설비용량0.0251.0000.7240.9390.6280.9370.6200.1970.422
풍력 전체 설비용량0.0060.7241.0000.6900.9210.6900.9250.1260.620
전기저장장치 연계 태양광 설비용량0.0450.9390.6901.0000.6100.9980.5950.1720.439
전기저장장치 연계 풍력 설비용량0.0050.6280.9210.6101.0000.6150.9940.1580.718
태양광 연계 전기저장장치 설비용량0.0480.9370.6900.9980.6151.0000.6000.1680.461
풍력 연계 전기저장장치 설비용량0.0030.6200.9250.5950.9940.6001.0000.1180.805
연도0.0570.1970.1260.1720.1580.1680.1181.0000.000
지역0.0000.4220.6200.4390.7180.4610.8050.0001.000

Missing values

2023-12-12T23:04:24.959178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:04:25.126226image/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

연도월지역태양광 전체 설비용량풍력 전체 설비용량전기저장장치 연계 태양광 설비용량전기저장장치 연계 풍력 설비용량태양광 연계 전기저장장치 설비용량풍력 연계 전기저장장치 설비용량
020181경기도106.85.32.03.02.00.1
120181강원도152.5323.310.548.08.99.0
220181경상남도164.147.817.314.015.43.0
320181경상북도256.4337.023.190.321.223.8
420181전라남도619.6206.247.675.840.69.3
520181전라북도130.819.523.75.015.01.2
620181충청남도287.22.02.90.01.90.0
720181충청북도92.80.08.70.05.60.0
820181제주도122.3272.80.077.00.013.5
920181서울특별시11.20.00.10.00.10.0
연도월지역태양광 전체 설비용량풍력 전체 설비용량전기저장장치 연계 태양광 설비용량전기저장장치 연계 풍력 설비용량태양광 연계 전기저장장치 설비용량풍력 연계 전기저장장치 설비용량
95920229충청북도331.4219750.099.9499850.087.1230.0
96020229제주도338.343045294.619.447475107.014.33922.5
96120229서울특별시15.9370550.00.295120.00.250.0
96220229인천광역시46.499349.012.3116946.011.458.0
96320229대전광역시14.57640.00.00.00.080.0
96420229광주광역시62.8242160.06.539710.04.970.0
96520229대구광역시51.251630.06.885120.06.00.0
96620229세종특별자치시28.147290.03.455310.03.250.0
96720229울산광역시37.009631.653.970080.01.8250.0
96820229부산광역시85.462260.757.114180.06.20.0