Overview

Dataset statistics

Number of variables15
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.0 KiB
Average record size in memory130.7 B

Variable types

Categorical5
Numeric10

Dataset

Description인천광역시 부평구 공공시설 태양광시설물에 관한 데이터입니다.시설명, 소재지, 설비용량 데이터를 제공합니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15062390&srcSe=7661IVAWM27C61E190

Alerts

가동일 is highly overall correlated with 위도 and 7 other fieldsHigh correlation
장비명 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
주소 is highly overall correlated with 위도 and 7 other fieldsHigh correlation
위치 is highly overall correlated with 위도 and 7 other fieldsHigh correlation
위도 is highly overall correlated with 위치 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 위치 and 2 other fieldsHigh correlation
모듈용량 is highly overall correlated with 인버터용량 and 5 other fieldsHigh correlation
인버터용량 is highly overall correlated with 모듈용량 and 5 other fieldsHigh correlation
시설용량(kw) is highly overall correlated with 모듈용량 and 5 other fieldsHigh correlation
평균 발전시간 is highly overall correlated with 최대 발전시간 and 3 other fieldsHigh correlation
최대 발전시간 is highly overall correlated with 평균 발전시간 and 3 other fieldsHigh correlation
전력량 합계 is highly overall correlated with 평균 발전시간 and 3 other fieldsHigh correlation
이산화탄소 절감량 합계 is highly overall correlated with 평균 발전시간 and 3 other fieldsHigh correlation
소나무(30년생) is highly overall correlated with 평균 발전시간 and 3 other fieldsHigh correlation
평균 발전시간 has 50 (27.8%) zerosZeros
최대 발전시간 has 50 (27.8%) zerosZeros
전력량 합계 has 50 (27.8%) zerosZeros
이산화탄소 절감량 합계 has 50 (27.8%) zerosZeros
소나무(30년생) has 50 (27.8%) zerosZeros

Reproduction

Analysis started2024-01-28 16:55:34.215340
Analysis finished2024-01-28 16:55:43.093472
Duration8.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일자
Categorical

Distinct12
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2019-01-01
15 
2019-06-01
15 
2019-07-01
15 
2019-08-01
15 
2019-02-01
15 
Other values (7)
105 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-01-01
2nd row2019-01-01
3rd row2019-01-01
4th row2019-01-01
5th row2019-01-01

Common Values

ValueCountFrequency (%)
2019-01-01 15
8.3%
2019-06-01 15
8.3%
2019-07-01 15
8.3%
2019-08-01 15
8.3%
2019-02-01 15
8.3%
2019-09-01 15
8.3%
2019-12-01 15
8.3%
2019-03-01 15
8.3%
2019-10-01 15
8.3%
2019-04-01 15
8.3%
Other values (2) 30
16.7%

Length

2024-01-29T01:55:43.144440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-01-01 15
8.3%
2019-06-01 15
8.3%
2019-07-01 15
8.3%
2019-08-01 15
8.3%
2019-02-01 15
8.3%
2019-09-01 15
8.3%
2019-12-01 15
8.3%
2019-03-01 15
8.3%
2019-10-01 15
8.3%
2019-04-01 15
8.3%
Other values (2) 30
16.7%

위치
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
갈산1동행정복지센터
 
12
갈산도서관
 
12
부개1동행정복지센터
 
12
부개2동행정복지센터
 
12
부개도서관
 
12
Other values (10)
120 

Length

Max length10
Median length9
Mean length7.9333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row갈산1동행정복지센터
2nd row갈산도서관
3rd row부개1동행정복지센터
4th row부개2동행정복지센터
5th row부개도서관

Common Values

ValueCountFrequency (%)
갈산1동행정복지센터 12
 
6.7%
갈산도서관 12
 
6.7%
부개1동행정복지센터 12
 
6.7%
부개2동행정복지센터 12
 
6.7%
부개도서관 12
 
6.7%
부평구의회 12
 
6.7%
부평기후변화체험관 12
 
6.7%
부평아트센터 12
 
6.7%
부평청소년수련원 12
 
6.7%
십정2동행정복지센터 12
 
6.7%
Other values (5) 60
33.3%

Length

2024-01-29T01:55:43.242868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
갈산1동행정복지센터 12
 
6.7%
갈산도서관 12
 
6.7%
부개1동행정복지센터 12
 
6.7%
부개2동행정복지센터 12
 
6.7%
부개도서관 12
 
6.7%
부평구의회 12
 
6.7%
부평기후변화체험관 12
 
6.7%
부평아트센터 12
 
6.7%
부평청소년수련원 12
 
6.7%
십정2동행정복지센터 12
 
6.7%
Other values (5) 60
33.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.502209
Minimum37.473828
Maximum37.518661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:43.334248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.473828
5-th percentile37.473828
Q137.485381
median37.508277
Q337.514758
95-th percentile37.518661
Maximum37.518661
Range0.044833
Interquartile range (IQR)0.029377

Descriptive statistics

Standard deviation0.014610793
Coefficient of variation (CV)0.00038959818
Kurtosis-1.2069937
Mean37.502209
Median Absolute Deviation (MAD)0.009564
Skewness-0.51394662
Sum6750.3976
Variance0.00021347526
MonotonicityNot monotonic
2024-01-29T01:55:43.440175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
37.514758 24
13.3%
37.517788 12
 
6.7%
37.517841 12
 
6.7%
37.485088 12
 
6.7%
37.4947778 12
 
6.7%
37.490958 12
 
6.7%
37.5067667 12
 
6.7%
37.509436 12
 
6.7%
37.483406 12
 
6.7%
37.508277 12
 
6.7%
Other values (4) 48
26.7%
ValueCountFrequency (%)
37.473828 12
6.7%
37.483406 12
6.7%
37.485088 12
6.7%
37.485381 12
6.7%
37.490958 12
6.7%
37.4947778 12
6.7%
37.5067667 12
6.7%
37.508277 12
6.7%
37.509436 12
6.7%
37.511411 12
6.7%
ValueCountFrequency (%)
37.518661 12
6.7%
37.517841 12
6.7%
37.517788 12
6.7%
37.514758 24
13.3%
37.511411 12
6.7%
37.509436 12
6.7%
37.508277 12
6.7%
37.5067667 12
6.7%
37.4947778 12
6.7%
37.490958 12
6.7%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.72602
Minimum126.70448
Maximum126.74022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:43.540853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.70448
5-th percentile126.70448
Q1126.70783
median126.73081
Q3126.73712
95-th percentile126.74022
Maximum126.74022
Range0.035743
Interquartile range (IQR)0.0292848

Descriptive statistics

Standard deviation0.013415068
Coefficient of variation (CV)0.00010585883
Kurtosis-1.1618872
Mean126.72602
Median Absolute Deviation (MAD)0.006888
Skewness-0.68406116
Sum22810.684
Variance0.00017996406
MonotonicityNot monotonic
2024-01-29T01:55:43.643094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
126.704482 24
13.3%
126.727173 12
 
6.7%
126.727367 12
 
6.7%
126.737 12
 
6.7%
126.7371188 12
 
6.7%
126.739488 12
 
6.7%
126.7218573 12
 
6.7%
126.730812 12
 
6.7%
126.70478 12
 
6.7%
126.740225 12
 
6.7%
Other values (4) 48
26.7%
ValueCountFrequency (%)
126.704482 24
13.3%
126.70478 12
6.7%
126.707834 12
6.7%
126.7218573 12
6.7%
126.727173 12
6.7%
126.727367 12
6.7%
126.730812 12
6.7%
126.733308 12
6.7%
126.736741 12
6.7%
126.737 12
6.7%
ValueCountFrequency (%)
126.740225 12
6.7%
126.739488 12
6.7%
126.7377 12
6.7%
126.7371188 12
6.7%
126.737 12
6.7%
126.736741 12
6.7%
126.733308 12
6.7%
126.730812 12
6.7%
126.727367 12
6.7%
126.727173 12
6.7%

주소
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
인천광역시 부평구 주부토로 254
 
12
인천광역시 부평구 갈산동 74-19
 
12
인천광역시 부평구 부개1동 257-6
 
12
인천광역시 부평구 동수천로 104
 
12
인천광역시 부평구 부일로83번길 46
 
12
Other values (10)
120 

Length

Max length22
Median length20
Mean length18.866667
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천광역시 부평구 주부토로 254
2nd row인천광역시 부평구 갈산동 74-19
3rd row인천광역시 부평구 부개1동 257-6
4th row인천광역시 부평구 동수천로 104
5th row인천광역시 부평구 부일로83번길 46

Common Values

ValueCountFrequency (%)
인천광역시 부평구 주부토로 254 12
 
6.7%
인천광역시 부평구 갈산동 74-19 12
 
6.7%
인천광역시 부평구 부개1동 257-6 12
 
6.7%
인천광역시 부평구 동수천로 104 12
 
6.7%
인천광역시 부평구 부일로83번길 46 12
 
6.7%
인천광역시 부평구 부평대로 168 12
 
6.7%
인천광역시 부평구 갈산동 403 12
 
6.7%
인천광역시 부평구 십정동 186-411 12
 
6.7%
인천광역시 부평구 삼산동 458-2 12
 
6.7%
인천광역시 부평구 십정2동 584 12
 
6.7%
Other values (5) 60
33.3%

Length

2024-01-29T01:55:44.022874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부평구 180
25.0%
인천광역시 168
23.3%
삼산동 24
 
3.3%
갈산동 24
 
3.3%
후정동로 12
 
1.7%
인전광역시 12
 
1.7%
68 12
 
1.7%
부흥로173번길 12
 
1.7%
441-6 12
 
1.7%
178-44 12
 
1.7%
Other values (21) 252
35.0%

장비명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
eKEN_HANWHA
72 
GESOL_STD
72 
eKEN820N
36 

Length

Max length11
Median length9
Mean length9.6
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweKEN_HANWHA
2nd rowGESOL_STD
3rd roweKEN_HANWHA
4th roweKEN_HANWHA
5th rowGESOL_STD

Common Values

ValueCountFrequency (%)
eKEN_HANWHA 72
40.0%
GESOL_STD 72
40.0%
eKEN820N 36
20.0%

Length

2024-01-29T01:55:44.137968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:55:44.235424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
eken_hanwha 72
40.0%
gesol_std 72
40.0%
eken820n 36
20.0%

모듈용량
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.053333
Minimum10.2
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:44.329371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.2
5-th percentile10.2
Q115
median23
Q335
95-th percentile52
Maximum52
Range41.8
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.946858
Coefficient of variation (CV)0.45855392
Kurtosis-0.60186539
Mean26.053333
Median Absolute Deviation (MAD)10.4
Skewness0.53307219
Sum4689.6
Variance142.72742
MonotonicityNot monotonic
2024-01-29T01:55:44.422137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12.0 12
 
6.7%
15.0 12
 
6.7%
10.2 12
 
6.7%
23.0 12
 
6.7%
20.0 12
 
6.7%
31.5 12
 
6.7%
36.5 12
 
6.7%
52.0 12
 
6.7%
33.4 12
 
6.7%
12.4 12
 
6.7%
Other values (5) 60
33.3%
ValueCountFrequency (%)
10.2 12
6.7%
12.0 12
6.7%
12.4 12
6.7%
15.0 12
6.7%
18.0 12
6.7%
20.0 12
6.7%
21.6 12
6.7%
23.0 12
6.7%
27.0 12
6.7%
31.5 12
6.7%
ValueCountFrequency (%)
52.0 12
6.7%
43.2 12
6.7%
36.5 12
6.7%
35.0 12
6.7%
33.4 12
6.7%
31.5 12
6.7%
27.0 12
6.7%
23.0 12
6.7%
21.6 12
6.7%
20.0 12
6.7%

인버터용량
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.96
Minimum10.2
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:44.517551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.2
5-th percentile10.2
Q112.4
median23
Q335
95-th percentile52
Maximum52
Range41.8
Interquartile range (IQR)22.6

Descriptive statistics

Standard deviation11.96687
Coefficient of variation (CV)0.47944193
Kurtosis-0.33667512
Mean24.96
Median Absolute Deviation (MAD)10.6
Skewness0.72904452
Sum4492.8
Variance143.20599
MonotonicityNot monotonic
2024-01-29T01:55:44.624271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12.0 12
 
6.7%
12.2 12
 
6.7%
10.2 12
 
6.7%
23.0 12
 
6.7%
19.0 12
 
6.7%
31.5 12
 
6.7%
36.0 12
 
6.7%
52.0 12
 
6.7%
24.3 12
 
6.7%
12.4 12
 
6.7%
Other values (5) 60
33.3%
ValueCountFrequency (%)
10.2 12
6.7%
12.0 12
6.7%
12.2 12
6.7%
12.4 12
6.7%
18.0 12
6.7%
19.0 12
6.7%
21.6 12
6.7%
23.0 12
6.7%
24.0 12
6.7%
24.3 12
6.7%
ValueCountFrequency (%)
52.0 12
6.7%
43.2 12
6.7%
36.0 12
6.7%
35.0 12
6.7%
31.5 12
6.7%
24.3 12
6.7%
24.0 12
6.7%
23.0 12
6.7%
21.6 12
6.7%
19.0 12
6.7%

가동일
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
48 
2012-11-01
36 
2016-06-20
24 
2013-12-01
24 
2010-12-01
12 
Other values (3)
36 

Length

Max length10
Median length10
Mean length8.4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-06-20
2nd row<NA>
3rd row<NA>
4th row2013-12-01
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 48
26.7%
2012-11-01 36
20.0%
2016-06-20 24
13.3%
2013-12-01 24
13.3%
2010-12-01 12
 
6.7%
2011-05-01 12
 
6.7%
2012-05-01 12
 
6.7%
2015-05-13 12
 
6.7%

Length

2024-01-29T01:55:44.723547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:55:44.820606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 48
26.7%
2012-11-01 36
20.0%
2016-06-20 24
13.3%
2013-12-01 24
13.3%
2010-12-01 12
 
6.7%
2011-05-01 12
 
6.7%
2012-05-01 12
 
6.7%
2015-05-13 12
 
6.7%

시설용량(kw)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.933333
Minimum10
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:44.919281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q112
median23
Q335
95-th percentile52
Maximum52
Range42
Interquartile range (IQR)23

Descriptive statistics

Standard deviation12.019351
Coefficient of variation (CV)0.48205954
Kurtosis-0.36593347
Mean24.933333
Median Absolute Deviation (MAD)11
Skewness0.70502915
Sum4488
Variance144.4648
MonotonicityNot monotonic
2024-01-29T01:55:45.007852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 36
20.0%
24 24
13.3%
10 12
 
6.7%
23 12
 
6.7%
19 12
 
6.7%
32 12
 
6.7%
36 12
 
6.7%
52 12
 
6.7%
35 12
 
6.7%
22 12
 
6.7%
Other values (2) 24
13.3%
ValueCountFrequency (%)
10 12
 
6.7%
12 36
20.0%
18 12
 
6.7%
19 12
 
6.7%
22 12
 
6.7%
23 12
 
6.7%
24 24
13.3%
32 12
 
6.7%
35 12
 
6.7%
36 12
 
6.7%
ValueCountFrequency (%)
52 12
6.7%
43 12
6.7%
36 12
6.7%
35 12
6.7%
32 12
6.7%
24 24
13.3%
23 12
6.7%
22 12
6.7%
19 12
6.7%
18 12
6.7%

평균 발전시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9538889
Minimum0
Maximum5.2
Zeros50
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:45.116635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.2
Q33.2
95-th percentile4.3
Maximum5.2
Range5.2
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5120508
Coefficient of variation (CV)0.77386734
Kurtosis-1.2225499
Mean1.9538889
Median Absolute Deviation (MAD)1.25
Skewness-0.0044659114
Sum351.7
Variance2.2862976
MonotonicityNot monotonic
2024-01-29T01:55:45.269192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 50
27.8%
2.5 7
 
3.9%
3.4 7
 
3.9%
1.8 7
 
3.9%
2.7 7
 
3.9%
3.6 6
 
3.3%
2.6 5
 
2.8%
3.9 5
 
2.8%
3.3 5
 
2.8%
3.1 5
 
2.8%
Other values (36) 76
42.2%
ValueCountFrequency (%)
0.0 50
27.8%
0.2 1
 
0.6%
0.3 1
 
0.6%
0.6 1
 
0.6%
0.7 3
 
1.7%
0.8 2
 
1.1%
0.9 1
 
0.6%
1.0 1
 
0.6%
1.1 1
 
0.6%
1.2 1
 
0.6%
ValueCountFrequency (%)
5.2 2
 
1.1%
4.7 1
 
0.6%
4.6 2
 
1.1%
4.5 2
 
1.1%
4.4 1
 
0.6%
4.3 3
1.7%
4.2 1
 
0.6%
4.1 1
 
0.6%
4.0 1
 
0.6%
3.9 5
2.8%

최대 발전시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3638889
Minimum0
Maximum10.1
Zeros50
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:45.405896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.1
Q35.225
95-th percentile6
Maximum10.1
Range10.1
Interquartile range (IQR)5.225

Descriptive statistics

Standard deviation2.3495813
Coefficient of variation (CV)0.69847173
Kurtosis-1.0628453
Mean3.3638889
Median Absolute Deviation (MAD)1.4
Skewness-0.36488504
Sum605.5
Variance5.5205323
MonotonicityNot monotonic
2024-01-29T01:55:45.528328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 50
27.8%
5.2 9
 
5.0%
5.5 7
 
3.9%
5.3 7
 
3.9%
4.7 7
 
3.9%
5.8 6
 
3.3%
5.7 6
 
3.3%
3.7 5
 
2.8%
5.9 5
 
2.8%
3.3 5
 
2.8%
Other values (34) 73
40.6%
ValueCountFrequency (%)
0.0 50
27.8%
1.0 2
 
1.1%
1.7 2
 
1.1%
2.1 1
 
0.6%
2.2 1
 
0.6%
2.5 1
 
0.6%
2.6 1
 
0.6%
2.8 1
 
0.6%
2.9 3
 
1.7%
3.3 5
 
2.8%
ValueCountFrequency (%)
10.1 1
 
0.6%
6.9 1
 
0.6%
6.8 1
 
0.6%
6.5 1
 
0.6%
6.4 2
 
1.1%
6.3 1
 
0.6%
6.1 1
 
0.6%
6.0 2
 
1.1%
5.9 5
2.8%
5.8 6
3.3%

전력량 합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1425.9667
Minimum0
Maximum7104
Zeros50
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:45.650698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1214
Q32146.25
95-th percentile4149.55
Maximum7104
Range7104
Interquartile range (IQR)2146.25

Descriptive statistics

Standard deviation1393.4161
Coefficient of variation (CV)0.97717299
Kurtosis1.3559453
Mean1425.9667
Median Absolute Deviation (MAD)1194
Skewness1.131055
Sum256674
Variance1941608.4
MonotonicityNot monotonic
2024-01-29T01:55:45.772566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
27.8%
1288 2
 
1.1%
1203 2
 
1.1%
3099 1
 
0.6%
1290 1
 
0.6%
1584 1
 
0.6%
936 1
 
0.6%
1876 1
 
0.6%
1760 1
 
0.6%
945 1
 
0.6%
Other values (119) 119
66.1%
ValueCountFrequency (%)
0 50
27.8%
194 1
 
0.6%
218 1
 
0.6%
250 1
 
0.6%
287 1
 
0.6%
321 1
 
0.6%
512 1
 
0.6%
515 1
 
0.6%
581 1
 
0.6%
594 1
 
0.6%
ValueCountFrequency (%)
7104 1
0.6%
5930 1
0.6%
5297 1
0.6%
5041 1
0.6%
4920 1
0.6%
4587 1
0.6%
4376 1
0.6%
4314 1
0.6%
4160 1
0.6%
4149 1
0.6%

이산화탄소 절감량 합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean670.91556
Minimum0
Maximum3342.4
Zeros50
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:45.887516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median571.2
Q31009.85
95-th percentile1952.36
Maximum3342.4
Range3342.4
Interquartile range (IQR)1009.85

Descriptive statistics

Standard deviation655.60098
Coefficient of variation (CV)0.97717361
Kurtosis1.3559321
Mean670.91556
Median Absolute Deviation (MAD)561.75
Skewness1.1310536
Sum120764.8
Variance429812.64
MonotonicityNot monotonic
2024-01-29T01:55:46.007421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 50
27.8%
606.0 2
 
1.1%
566.0 2
 
1.1%
1458.1 1
 
0.6%
606.9 1
 
0.6%
745.3 1
 
0.6%
440.4 1
 
0.6%
882.7 1
 
0.6%
828.1 1
 
0.6%
444.6 1
 
0.6%
Other values (119) 119
66.1%
ValueCountFrequency (%)
0.0 50
27.8%
91.3 1
 
0.6%
102.6 1
 
0.6%
117.6 1
 
0.6%
135.0 1
 
0.6%
151.0 1
 
0.6%
240.9 1
 
0.6%
242.3 1
 
0.6%
273.4 1
 
0.6%
279.5 1
 
0.6%
ValueCountFrequency (%)
3342.4 1
0.6%
2790.1 1
0.6%
2492.2 1
0.6%
2371.8 1
0.6%
2314.9 1
0.6%
2158.2 1
0.6%
2058.9 1
0.6%
2029.7 1
0.6%
1957.3 1
0.6%
1952.1 1
0.6%

소나무(30년생)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct115
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.378889
Minimum0
Maximum96.5
Zeros50
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-29T01:55:46.142901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16.5
Q329.15
95-th percentile56.405
Maximum96.5
Range96.5
Interquartile range (IQR)29.15

Descriptive statistics

Standard deviation18.93466
Coefficient of variation (CV)0.97707665
Kurtosis1.3546639
Mean19.378889
Median Absolute Deviation (MAD)16.2
Skewness1.1308706
Sum3488.2
Variance358.52134
MonotonicityNot monotonic
2024-01-29T01:55:46.264864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 50
27.8%
17.5 4
 
2.2%
16.3 3
 
1.7%
12.7 3
 
1.7%
9.4 2
 
1.1%
32.7 2
 
1.1%
27.1 2
 
1.1%
7.0 2
 
1.1%
42.2 2
 
1.1%
16.6 2
 
1.1%
Other values (105) 108
60.0%
ValueCountFrequency (%)
0.0 50
27.8%
2.6 1
 
0.6%
3.0 1
 
0.6%
3.4 1
 
0.6%
3.9 1
 
0.6%
4.4 1
 
0.6%
7.0 2
 
1.1%
7.9 1
 
0.6%
8.1 1
 
0.6%
8.2 2
 
1.1%
ValueCountFrequency (%)
96.5 1
0.6%
80.6 1
0.6%
72.0 1
0.6%
68.5 1
0.6%
66.9 1
0.6%
62.3 1
0.6%
59.5 1
0.6%
58.6 1
0.6%
56.5 1
0.6%
56.4 1
0.6%

Interactions

2024-01-29T01:55:42.058340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:34.854854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.547025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.341420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.007755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.719173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.723544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.521835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.364620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.204514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.141456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:34.920526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.627106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.403910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.072084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.786759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.793358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.633850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.443678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.287052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.239990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:34.998741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.725264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.479758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.167775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.856121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.879829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.743034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.539970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.382279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.303644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.057991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.793409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.542528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.237758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.913769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.953430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.826360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.637340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.465218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.372940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.122619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.862111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.606158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.308123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.975740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.027587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.913207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.721029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.541312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.439436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.194879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.933055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.671879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.370429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.038161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.100040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.989910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.805702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.620386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.508759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.259433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.015285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.735795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.433700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.103745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.173815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.067643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.891414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.697102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.580407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.326892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.097552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.803987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.500739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.174578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.246458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.131343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.966170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.813590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.661590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.399015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.172284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.874337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.577486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.276201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.323952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.207830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.041156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.902642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:42.739631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:35.472323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.263977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:36.943779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:37.654598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:38.360409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:39.430722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:40.285972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.124900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:55:41.982401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:55:46.362257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자위치위도경도주소장비명모듈용량인버터용량가동일시설용량(kw)평균 발전시간최대 발전시간전력량 합계이산화탄소 절감량 합계소나무(30년생)
기준일자1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5690.4480.0000.0000.000
위치0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.6100.6820.7300.7300.730
위도0.0001.0001.0000.8151.0000.7480.9140.8530.8880.8530.4440.4060.5000.5000.500
경도0.0001.0000.8151.0001.0000.8160.9320.9020.8560.9020.3990.3680.5320.5320.532
주소0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.6100.6820.7300.7300.730
장비명0.0001.0000.7480.8161.0001.0000.9660.7850.8430.7850.4000.4620.5160.5160.516
모듈용량0.0001.0000.9140.9321.0000.9661.0000.9700.9490.9700.4730.4980.6600.6600.660
인버터용량0.0001.0000.8530.9021.0000.7850.9701.0000.9181.0000.4420.6180.6680.6680.668
가동일0.0001.0000.8880.8561.0000.8430.9490.9181.0000.9180.4730.7340.5370.5370.537
시설용량(kw)0.0001.0000.8530.9021.0000.7850.9701.0000.9181.0000.4420.6180.6680.6680.668
평균 발전시간0.5690.6100.4440.3990.6100.4000.4730.4420.4730.4421.0000.8210.7930.7930.793
최대 발전시간0.4480.6820.4060.3680.6820.4620.4980.6180.7340.6180.8211.0000.5770.5770.577
전력량 합계0.0000.7300.5000.5320.7300.5160.6600.6680.5370.6680.7930.5771.0001.0001.000
이산화탄소 절감량 합계0.0000.7300.5000.5320.7300.5160.6600.6680.5370.6680.7930.5771.0001.0001.000
소나무(30년생)0.0000.7300.5000.5320.7300.5160.6600.6680.5370.6680.7930.5771.0001.0001.000
2024-01-29T01:55:46.492102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가동일장비명기준일자주소위치
가동일1.0000.8030.0000.9840.984
장비명0.8031.0000.0000.9660.966
기준일자0.0000.0001.0000.0000.000
주소0.9840.9660.0001.0001.000
위치0.9840.9660.0001.0001.000
2024-01-29T01:55:46.585557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도모듈용량인버터용량시설용량(kw)평균 발전시간최대 발전시간전력량 합계이산화탄소 절감량 합계소나무(30년생)기준일자위치주소장비명가동일
위도1.000-0.045-0.150-0.200-0.152-0.148-0.163-0.316-0.316-0.3160.0000.9770.9770.6700.818
경도-0.0451.000-0.064-0.111-0.128-0.050-0.071-0.043-0.043-0.0430.0000.9740.9740.4960.716
모듈용량-0.150-0.0641.0000.9930.990-0.084-0.2010.3020.3020.3020.0000.9820.9820.7660.878
인버터용량-0.200-0.1110.9931.0000.996-0.090-0.2030.3020.3020.3020.0000.9790.9790.6930.793
시설용량(kw)-0.152-0.1280.9900.9961.000-0.097-0.2100.2940.2940.2940.0000.9790.9790.6930.793
평균 발전시간-0.148-0.050-0.084-0.090-0.0971.0000.8860.8530.8530.8530.2820.2710.2710.2620.245
최대 발전시간-0.163-0.071-0.201-0.203-0.2100.8861.0000.7180.7180.7180.2040.3660.3660.3270.335
전력량 합계-0.316-0.0430.3020.3020.2940.8530.7181.0001.0001.0000.0000.3680.3680.3540.304
이산화탄소 절감량 합계-0.316-0.0430.3020.3020.2940.8530.7181.0001.0001.0000.0000.3680.3680.3540.304
소나무(30년생)-0.316-0.0430.3020.3020.2940.8530.7181.0001.0001.0000.0000.3680.3680.3540.304
기준일자0.0000.0000.0000.0000.0000.2820.2040.0000.0000.0001.0000.0000.0000.0000.000
위치0.9770.9740.9820.9790.9790.2710.3660.3680.3680.3680.0001.0001.0000.9660.984
주소0.9770.9740.9820.9790.9790.2710.3660.3680.3680.3680.0001.0001.0000.9660.984
장비명0.6700.4960.7660.6930.6930.2620.3270.3540.3540.3540.0000.9660.9661.0000.803
가동일0.8180.7160.8780.7930.7930.2450.3350.3040.3040.3040.0000.9840.9840.8031.000

Missing values

2024-01-29T01:55:42.852404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:55:43.034837image/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

기준일자위치위도경도주소장비명모듈용량인버터용량가동일시설용량(kw)평균 발전시간최대 발전시간전력량 합계이산화탄소 절감량 합계소나무(30년생)
02019-01-01갈산1동행정복지센터37.517788126.727173인천광역시 부평구 주부토로 254eKEN_HANWHA12.012.02016-06-20122.53.7945444.612.8
12019-01-01갈산도서관37.517841126.727367인천광역시 부평구 갈산동 74-19GESOL_STD15.012.2<NA>122.53.7935439.912.7
22019-01-01부개1동행정복지센터37.485088126.737인천광역시 부평구 부개1동 257-6eKEN_HANWHA10.210.2<NA>100.00.000.00.0
32019-01-01부개2동행정복지센터37.494778126.737119인천광역시 부평구 동수천로 104eKEN_HANWHA23.023.02013-12-01232.53.41763829.524.0
42019-01-01부개도서관37.490958126.739488인천광역시 부평구 부일로83번길 46GESOL_STD20.019.0<NA>192.94.21711805.023.3
52019-01-01부평구의회37.506767126.721857인천광역시 부평구 부평대로 168GESOL_STD31.531.52013-12-01322.53.524381147.133.1
62019-01-01부평기후변화체험관37.509436126.730812인천광역시 부평구 갈산동 403GESOL_STD36.536.02012-11-01362.63.828711350.839.0
72019-06-01부개1동행정복지센터37.485088126.737인천광역시 부평구 부개1동 257-6eKEN_HANWHA10.210.2<NA>100.00.000.00.0
82019-01-01부평아트센터37.483406126.70478인천광역시 부평구 십정동 186-411GESOL_STD52.052.02012-11-01520.72.91113523.715.1
92019-01-01부평청소년수련원37.508277126.740225인천광역시 부평구 삼산동 458-2GESOL_STD33.424.32010-12-01241.41.71039488.814.1
기준일자위치위도경도주소장비명모듈용량인버터용량가동일시설용량(kw)평균 발전시간최대 발전시간전력량 합계이산화탄소 절감량 합계소나무(30년생)
1702019-12-01부평구의회37.506767126.721857인천광역시 부평구 부평대로 168GESOL_STD31.531.52013-12-01321.73.31616760.322.0
1712019-12-01부평기후변화체험관37.509436126.730812인천광역시 부평구 갈산동 403GESOL_STD36.536.02012-11-01361.93.321331003.629.0
1722019-11-01은광원37.485381126.736741인천광역시 부평구 부개동 257eKEN820N35.035.02012-11-01350.21.019491.32.6
1732019-12-01부평아트센터37.483406126.70478인천광역시 부평구 십정동 186-411GESOL_STD52.052.02012-11-01521.82.929471386.640.0
1742019-12-01부평청소년수련원37.508277126.740225인천광역시 부평구 삼산동 458-2GESOL_STD33.424.32010-12-01241.11.7827389.111.2
1752019-10-01청천2동행정복지센터37.514758126.704482인천광역시 부평구 청천2동 178-44eKEN820N21.621.62011-05-01220.00.000.00.0
1762019-11-01청천2동행정복지센터37.514758126.704482인천광역시 부평구 청천2동 178-44eKEN820N21.621.62011-05-01220.00.000.00.0
1772019-12-01십정2동행정복지센터37.473828126.707834인천광역시 부평구 십정2동 584eKEN_HANWHA12.412.4<NA>121.53.6581273.47.9
1782019-12-01은광원37.485381126.736741인천광역시 부평구 부개동 257eKEN820N35.035.02012-11-01350.61.0644303.08.8
1792019-12-01청천2동행정복지센터37.514758126.704482인천광역시 부평구 청천2동 178-44eKEN820N21.621.62011-05-01220.00.000.00.0