Overview

Dataset statistics

Number of variables15
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory136.6 B

Variable types

Numeric11
Categorical2
Text2

Dataset

Description서울특별시 종로구 공공태양광 발전량 현황(단체명, 설치연도, 설치장소, 도로명주소, 설비용량, 설치비, 연간발전량)에 대한 데이터를 제공합니다.
Author서울특별시 종로구
URLhttps://www.data.go.kr/data/15126264/fileData.do

Alerts

단체명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 설치연도 and 1 other fieldsHigh correlation
설치연도 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
설비용량(KW) is highly overall correlated with 설치비(백만원) and 6 other fieldsHigh correlation
설치비(백만원) is highly overall correlated with 설비용량(KW) and 6 other fieldsHigh correlation
2016년 발전량(kWh) is highly overall correlated with 연번 and 3 other fieldsHigh correlation
2017년 발전량(kWh) is highly overall correlated with 설치연도 and 8 other fieldsHigh correlation
2018년 발전량(kWh) is highly overall correlated with 설비용량(KW) and 7 other fieldsHigh correlation
2019년 발전량(kWh) is highly overall correlated with 설비용량(KW) and 6 other fieldsHigh correlation
2020년 발전량(kWh) is highly overall correlated with 설비용량(KW) and 6 other fieldsHigh correlation
2021년 발전량(kWh) is highly overall correlated with 설비용량(KW) and 6 other fieldsHigh correlation
2022년 발전량(kWh) is highly overall correlated with 설비용량(KW) and 6 other fieldsHigh correlation
연번 has unique valuesUnique
설치장소 has unique valuesUnique
도로명주소 has unique valuesUnique
2020년 발전량(kWh) has unique valuesUnique
2022년 발전량(kWh) has unique valuesUnique
2016년 발전량(kWh) has 11 (47.8%) zerosZeros
2017년 발전량(kWh) has 9 (39.1%) zerosZeros
2018년 발전량(kWh) has 7 (30.4%) zerosZeros
2019년 발전량(kWh) has 3 (13.0%) zerosZeros
2020년 발전량(kWh) has 1 (4.3%) zerosZeros
2021년 발전량(kWh) has 1 (4.3%) zerosZeros

Reproduction

Analysis started2024-03-15 00:41:03.767550
Analysis finished2024-03-15 00:41:36.252158
Duration32.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:36.441517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q16.5
median12
Q317.5
95-th percentile21.9
Maximum23
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.56519417
Kurtosis-1.2
Mean12
Median Absolute Deviation (MAD)6
Skewness0
Sum276
Variance46
MonotonicityStrictly increasing
2024-03-15T09:41:36.812468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
23 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 1
 
4.3%
19 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 1
4.3%
5 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
ValueCountFrequency (%)
23 1
4.3%
22 1
4.3%
21 1
4.3%
20 1
4.3%
19 1
4.3%
18 1
4.3%
17 1
4.3%
16 1
4.3%
15 1
4.3%
14 1
4.3%

단체명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 B
종로구
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
종로구 23
100.0%

Length

2024-03-15T09:41:37.210196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:41:37.497914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 23
100.0%

설치연도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.087
Minimum2009
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:37.772078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12009.5
median2015
Q32017.5
95-th percentile2018
Maximum2021
Range12
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.9417698
Coefficient of variation (CV)0.0019571001
Kurtosis-1.4541638
Mean2014.087
Median Absolute Deviation (MAD)3
Skewness-0.14890424
Sum46324
Variance15.537549
MonotonicityIncreasing
2024-03-15T09:41:38.198860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2009 6
26.1%
2018 5
21.7%
2015 3
13.0%
2017 3
13.0%
2010 1
 
4.3%
2011 1
 
4.3%
2012 1
 
4.3%
2014 1
 
4.3%
2016 1
 
4.3%
2021 1
 
4.3%
ValueCountFrequency (%)
2009 6
26.1%
2010 1
 
4.3%
2011 1
 
4.3%
2012 1
 
4.3%
2014 1
 
4.3%
2015 3
13.0%
2016 1
 
4.3%
2017 3
13.0%
2018 5
21.7%
2021 1
 
4.3%
ValueCountFrequency (%)
2021 1
 
4.3%
2018 5
21.7%
2017 3
13.0%
2016 1
 
4.3%
2015 3
13.0%
2014 1
 
4.3%
2012 1
 
4.3%
2011 1
 
4.3%
2010 1
 
4.3%
2009 6
26.1%

설치장소
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-03-15T09:41:38.930054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length16
Mean length9.9130435
Min length5

Characters and Unicode

Total characters228
Distinct characters89
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row평창동주민센터
2nd row삼청동주민센터
3rd row종로노인복지관(별관)
4th row무악어린이집
5th row청운어린이집
ValueCountFrequency (%)
평창동주민센터 1
 
3.6%
삼청동주민센터 1
 
3.6%
착한주차안내소 1
 
3.6%
부암경로당 1
 
3.6%
와룡공영주차장 1
 
3.6%
인왕산도시자연공원청운화장실 1
 
3.6%
삼청공원공중화장실1 1
 
3.6%
환경미화원휴게소 1
 
3.6%
청사 1
 
3.6%
종로1234가동 1
 
3.6%
Other values (18) 18
64.3%
2024-03-15T09:41:39.884499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13
 
5.7%
10
 
4.4%
10
 
4.4%
8
 
3.5%
8
 
3.5%
7
 
3.1%
6
 
2.6%
6
 
2.6%
6
 
2.6%
5
 
2.2%
Other values (79) 149
65.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 212
93.0%
Space Separator 5
 
2.2%
Decimal Number 5
 
2.2%
Open Punctuation 3
 
1.3%
Close Punctuation 3
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13
 
6.1%
10
 
4.7%
10
 
4.7%
8
 
3.8%
8
 
3.8%
7
 
3.3%
6
 
2.8%
6
 
2.8%
6
 
2.8%
5
 
2.4%
Other values (72) 133
62.7%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
2 1
20.0%
3 1
20.0%
4 1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 212
93.0%
Common 16
 
7.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13
 
6.1%
10
 
4.7%
10
 
4.7%
8
 
3.8%
8
 
3.8%
7
 
3.3%
6
 
2.8%
6
 
2.8%
6
 
2.8%
5
 
2.4%
Other values (72) 133
62.7%
Common
ValueCountFrequency (%)
5
31.2%
( 3
18.8%
) 3
18.8%
1 2
 
12.5%
2 1
 
6.2%
3 1
 
6.2%
4 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 212
93.0%
ASCII 16
 
7.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
13
 
6.1%
10
 
4.7%
10
 
4.7%
8
 
3.8%
8
 
3.8%
7
 
3.3%
6
 
2.8%
6
 
2.8%
6
 
2.8%
5
 
2.4%
Other values (72) 133
62.7%
ASCII
ValueCountFrequency (%)
5
31.2%
( 3
18.8%
) 3
18.8%
1 2
 
12.5%
2 1
 
6.2%
3 1
 
6.2%
4 1
 
6.2%

도로명주소
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-03-15T09:41:40.653951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length17.913043
Min length16

Characters and Unicode

Total characters412
Distinct characters56
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row서울특별시 종로구 평창문화로 65
2nd row서울특별시 종로구 삼청동 1051
3rd row서울특별시 종로구 이화동251
4th row서울특별시 종로구 무악동 79
5th row서울특별시 종로구 청운동 63
ValueCountFrequency (%)
서울특별시 23
25.6%
종로구 23
25.6%
통일로14길 2
 
2.2%
청운동 2
 
2.2%
성균관로 1
 
1.1%
신문로2가 1
 
1.1%
인왕산로1길 1
 
1.1%
21 1
 
1.1%
36 1
 
1.1%
이화동 1
 
1.1%
Other values (34) 34
37.8%
2024-03-15T09:41:41.579993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
16.3%
34
 
8.3%
23
 
5.6%
23
 
5.6%
23
 
5.6%
23
 
5.6%
23
 
5.6%
23
 
5.6%
23
 
5.6%
1 15
 
3.6%
Other values (46) 135
32.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 269
65.3%
Decimal Number 76
 
18.4%
Space Separator 67
 
16.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
34
12.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
10
 
3.7%
8
 
3.0%
Other values (35) 56
20.8%
Decimal Number
ValueCountFrequency (%)
1 15
19.7%
3 10
13.2%
4 9
11.8%
7 8
10.5%
2 8
10.5%
6 7
9.2%
5 7
9.2%
0 6
 
7.9%
9 4
 
5.3%
8 2
 
2.6%
Space Separator
ValueCountFrequency (%)
67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 269
65.3%
Common 143
34.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
34
12.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
10
 
3.7%
8
 
3.0%
Other values (35) 56
20.8%
Common
ValueCountFrequency (%)
67
46.9%
1 15
 
10.5%
3 10
 
7.0%
4 9
 
6.3%
7 8
 
5.6%
2 8
 
5.6%
6 7
 
4.9%
5 7
 
4.9%
0 6
 
4.2%
9 4
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 269
65.3%
ASCII 143
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
46.9%
1 15
 
10.5%
3 10
 
7.0%
4 9
 
6.3%
7 8
 
5.6%
2 8
 
5.6%
6 7
 
4.9%
5 7
 
4.9%
0 6
 
4.2%
9 4
 
2.8%
Hangul
ValueCountFrequency (%)
34
12.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
23
8.6%
10
 
3.7%
8
 
3.0%
Other values (35) 56
20.8%

설비용량(KW)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.135652
Minimum3
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:42.010278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.46
Q14.7
median10
Q322.85
95-th percentile32.94
Maximum160
Range157
Interquartile range (IQR)18.15

Descriptive statistics

Standard deviation32.450031
Coefficient of variation (CV)1.6957892
Kurtosis17.790213
Mean19.135652
Median Absolute Deviation (MAD)6
Skewness4.0318152
Sum440.12
Variance1053.0045
MonotonicityNot monotonic
2024-03-15T09:41:42.467816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
10.0 5
21.7%
5.0 2
 
8.7%
3.46 2
 
8.7%
4.0 2
 
8.7%
5.2 1
 
4.3%
32.0 1
 
4.3%
160.0 1
 
4.3%
18.2 1
 
4.3%
27.5 1
 
4.3%
28.8 1
 
4.3%
Other values (6) 6
26.1%
ValueCountFrequency (%)
3.0 1
 
4.3%
3.46 2
 
8.7%
4.0 2
 
8.7%
4.4 1
 
4.3%
5.0 2
 
8.7%
5.2 1
 
4.3%
6.3 1
 
4.3%
10.0 5
21.7%
14.4 1
 
4.3%
18.2 1
 
4.3%
ValueCountFrequency (%)
160.0 1
 
4.3%
33.0 1
 
4.3%
32.4 1
 
4.3%
32.0 1
 
4.3%
28.8 1
 
4.3%
27.5 1
 
4.3%
18.2 1
 
4.3%
14.4 1
 
4.3%
10.0 5
21.7%
6.3 1
 
4.3%

설치비(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.434783
Minimum8
Maximum321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:43.072829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.5
Q136
median78
Q389.5
95-th percentile157.6
Maximum321
Range313
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation68.039979
Coefficient of variation (CV)0.87867463
Kurtosis6.8388185
Mean77.434783
Median Absolute Deviation (MAD)34
Skewness2.1750365
Sum1781
Variance4629.4387
MonotonicityNot monotonic
2024-03-15T09:41:43.486756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
78 2
 
8.7%
8 2
 
8.7%
136 2
 
8.7%
87 2
 
8.7%
65 1
 
4.3%
90 1
 
4.3%
321 1
 
4.3%
57 1
 
4.3%
14 1
 
4.3%
15 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
8 2
8.7%
13 1
4.3%
14 1
4.3%
15 1
4.3%
31 1
4.3%
41 1
4.3%
44 1
4.3%
45 1
4.3%
57 1
4.3%
65 1
4.3%
ValueCountFrequency (%)
321 1
4.3%
160 1
4.3%
136 2
8.7%
98 1
4.3%
90 1
4.3%
89 1
4.3%
87 2
8.7%
80 1
4.3%
78 2
8.7%
65 1
4.3%

2016년 발전량(kWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6409.1304
Minimum0
Maximum40501
Zeros11
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:43.876961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2386
Q39964
95-th percentile25063.4
Maximum40501
Range40501
Interquartile range (IQR)9964

Descriptive statistics

Standard deviation9924.2637
Coefficient of variation (CV)1.5484571
Kurtosis5.9857556
Mean6409.1304
Median Absolute Deviation (MAD)2386
Skewness2.3037604
Sum147410
Variance98491010
MonotonicityNot monotonic
2024-03-15T09:41:44.296808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 11
47.8%
5901 1
 
4.3%
9784 1
 
4.3%
10144 1
 
4.3%
11314 1
 
4.3%
2643 1
 
4.3%
11434 1
 
4.3%
5536 1
 
4.3%
2386 1
 
4.3%
12926 1
 
4.3%
Other values (3) 3
 
13.0%
ValueCountFrequency (%)
0 11
47.8%
2386 1
 
4.3%
2643 1
 
4.3%
5536 1
 
4.3%
5901 1
 
4.3%
8429 1
 
4.3%
9784 1
 
4.3%
10144 1
 
4.3%
11314 1
 
4.3%
11434 1
 
4.3%
ValueCountFrequency (%)
40501 1
4.3%
26412 1
4.3%
12926 1
4.3%
11434 1
4.3%
11314 1
4.3%
10144 1
4.3%
9784 1
4.3%
8429 1
4.3%
5901 1
4.3%
5536 1
4.3%

2017년 발전량(kWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8379.3043
Minimum0
Maximum37048
Zeros9
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:44.717385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5048
Q310901
95-th percentile32322.6
Maximum37048
Range37048
Interquartile range (IQR)10901

Descriptive statistics

Standard deviation10719.357
Coefficient of variation (CV)1.2792658
Kurtosis2.0529872
Mean8379.3043
Median Absolute Deviation (MAD)5048
Skewness1.6190676
Sum192724
Variance1.1490462 × 108
MonotonicityNot monotonic
2024-03-15T09:41:45.188780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 9
39.1%
5510 1
 
4.3%
10213 1
 
4.3%
10683 1
 
4.3%
11017 1
 
4.3%
3840 1
 
4.3%
11923 1
 
4.3%
5048 1
 
4.3%
27675 1
 
4.3%
12556 1
 
4.3%
Other values (5) 5
21.7%
ValueCountFrequency (%)
0 9
39.1%
3840 1
 
4.3%
4908 1
 
4.3%
5048 1
 
4.3%
5510 1
 
4.3%
8679 1
 
4.3%
10213 1
 
4.3%
10683 1
 
4.3%
10785 1
 
4.3%
11017 1
 
4.3%
ValueCountFrequency (%)
37048 1
4.3%
32839 1
4.3%
27675 1
4.3%
12556 1
4.3%
11923 1
4.3%
11017 1
4.3%
10785 1
4.3%
10683 1
4.3%
10213 1
4.3%
8679 1
4.3%

2018년 발전량(kWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11420.478
Minimum0
Maximum38660
Zeros7
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:45.641460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7248
Q316738.5
95-th percentile37347.5
Maximum38660
Range38660
Interquartile range (IQR)16738.5

Descriptive statistics

Standard deviation13071.949
Coefficient of variation (CV)1.1446061
Kurtosis-0.028466534
Mean11420.478
Median Absolute Deviation (MAD)7248
Skewness1.1206201
Sum262671
Variance1.7087586 × 108
MonotonicityNot monotonic
2024-03-15T09:41:46.070691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 7
30.4%
5850 1
 
4.3%
12880 1
 
4.3%
20597 1
 
4.3%
26275 1
 
4.3%
37010 1
 
4.3%
38660 1
 
4.3%
37385 1
 
4.3%
7248 1
 
4.3%
3148 1
 
4.3%
Other values (7) 7
30.4%
ValueCountFrequency (%)
0 7
30.4%
3148 1
 
4.3%
4560 1
 
4.3%
4760 1
 
4.3%
5850 1
 
4.3%
7248 1
 
4.3%
7814 1
 
4.3%
8858 1
 
4.3%
10200 1
 
4.3%
10571 1
 
4.3%
ValueCountFrequency (%)
38660 1
4.3%
37385 1
4.3%
37010 1
4.3%
26855 1
4.3%
26275 1
4.3%
20597 1
4.3%
12880 1
4.3%
10571 1
4.3%
10200 1
4.3%
8858 1
4.3%

2019년 발전량(kWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11593.522
Minimum0
Maximum37232
Zeros3
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:46.289366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13592
median7314
Q314255.5
95-th percentile36232.9
Maximum37232
Range37232
Interquartile range (IQR)10663.5

Descriptive statistics

Standard deviation12037.422
Coefficient of variation (CV)1.0382886
Kurtosis0.34621984
Mean11593.522
Median Absolute Deviation (MAD)4449
Skewness1.247013
Sum266651
Variance1.4489952 × 108
MonotonicityNot monotonic
2024-03-15T09:41:46.586483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3
 
13.0%
5904 1
 
4.3%
36106 1
 
4.3%
4687 1
 
4.3%
3694 1
 
4.3%
21578 1
 
4.3%
4079 1
 
4.3%
2800 1
 
4.3%
16748 1
 
4.3%
37232 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
0 3
13.0%
2143 1
 
4.3%
2800 1
 
4.3%
3490 1
 
4.3%
3694 1
 
4.3%
4079 1
 
4.3%
4316 1
 
4.3%
4687 1
 
4.3%
5904 1
 
4.3%
7314 1
 
4.3%
ValueCountFrequency (%)
37232 1
4.3%
36247 1
4.3%
36106 1
4.3%
28276 1
4.3%
21578 1
4.3%
16748 1
4.3%
11763 1
4.3%
10797 1
4.3%
10644 1
4.3%
9887 1
4.3%

2020년 발전량(kWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12625.429
Minimum0
Maximum37925
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:46.839479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3196.7165
Q14293
median9468.8746
Q315461.5
95-th percentile35159.561
Maximum37925
Range37925
Interquartile range (IQR)11168.5

Descriptive statistics

Standard deviation11431.805
Coefficient of variation (CV)0.90545869
Kurtosis0.26085752
Mean12625.429
Median Absolute Deviation (MAD)5553.8746
Skewness1.2193231
Sum290384.87
Variance1.3068616 × 108
MonotonicityNot monotonic
2024-03-15T09:41:47.057537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6040.0 1
 
4.3%
9468.874598 1
 
4.3%
0.0 1
 
4.3%
3915.0 1
 
4.3%
3437.0 1
 
4.3%
18263.0 1
 
4.3%
3688.0 1
 
4.3%
3448.0 1
 
4.3%
5146.098901 1
 
4.3%
22825.0 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0.0 1
4.3%
3170.018315 1
4.3%
3437.0 1
4.3%
3448.0 1
4.3%
3688.0 1
4.3%
3915.0 1
4.3%
4671.0 1
4.3%
5146.098901 1
4.3%
6040.0 1
4.3%
6264.27 1
4.3%
ValueCountFrequency (%)
37925.0 1
4.3%
35210.0 1
4.3%
34705.6129 1
4.3%
28992.0 1
4.3%
22825.0 1
4.3%
18263.0 1
4.3%
12660.0 1
4.3%
11684.0 1
4.3%
11199.0 1
4.3%
10929.0 1
4.3%

2021년 발전량(kWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11367.522
Minimum0
Maximum34881
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:47.271430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1185.9
Q13931.5
median6393
Q316333.5
95-th percentile33816.4
Maximum34881
Range34881
Interquartile range (IQR)12402

Descriptive statistics

Standard deviation10275.575
Coefficient of variation (CV)0.90394157
Kurtosis0.45595144
Mean11367.522
Median Absolute Deviation (MAD)4038
Skewness1.1778291
Sum261453
Variance1.0558745 × 108
MonotonicityNot monotonic
2024-03-15T09:41:47.509744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3498 2
 
8.7%
6253 1
 
4.3%
34881 1
 
4.3%
0 1
 
4.3%
4365 1
 
4.3%
20126 1
 
4.3%
2355 1
 
4.3%
4987 1
 
4.3%
21956 1
 
4.3%
34746 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
0 1
4.3%
1056 1
4.3%
2355 1
4.3%
3171 1
4.3%
3498 2
8.7%
4365 1
4.3%
4987 1
4.3%
5723 1
4.3%
5787 1
4.3%
6253 1
4.3%
ValueCountFrequency (%)
34881 1
4.3%
34746 1
4.3%
25450 1
4.3%
22232 1
4.3%
21956 1
4.3%
20126 1
4.3%
12541 1
4.3%
11896 1
4.3%
10975 1
4.3%
9793 1
4.3%

2022년 발전량(kWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19685.783
Minimum1622
Maximum186425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T09:41:47.784619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1622
5-th percentile2439.1
Q15054.5
median11165
Q319851
95-th percentile34766.8
Maximum186425
Range184803
Interquartile range (IQR)14796.5

Descriptive statistics

Standard deviation37505.433
Coefficient of variation (CV)1.9052041
Kurtosis19.90046
Mean19685.783
Median Absolute Deviation (MAD)6751
Skewness4.3405836
Sum452773
Variance1.4066575 × 109
MonotonicityNot monotonic
2024-03-15T09:41:48.305565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6305 1
 
4.3%
9982 1
 
4.3%
186425 1
 
4.3%
4414 1
 
4.3%
3503 1
 
4.3%
20241 1
 
4.3%
3331 1
 
4.3%
2340 1
 
4.3%
4925 1
 
4.3%
19461 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1622 1
4.3%
2340 1
4.3%
3331 1
4.3%
3503 1
4.3%
4414 1
4.3%
4925 1
4.3%
5184 1
4.3%
5337 1
4.3%
5927 1
4.3%
6305 1
4.3%
ValueCountFrequency (%)
186425 1
4.3%
35540 1
4.3%
27808 1
4.3%
25830 1
4.3%
23722 1
4.3%
20241 1
4.3%
19461 1
4.3%
13900 1
4.3%
12310 1
4.3%
11798 1
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-01-05
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-05
2nd row2024-01-05
3rd row2024-01-05
4th row2024-01-05
5th row2024-01-05

Common Values

ValueCountFrequency (%)
2024-01-05 23
100.0%

Length

2024-03-15T09:41:48.556186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:41:48.714936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-05 23
100.0%

Interactions

2024-03-15T09:41:32.209705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:04.426851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:06.827391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:09.755589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:12.989326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:15.448689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:18.231297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:20.741295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:23.714028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:26.768195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:29.405740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:32.741711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:04.659856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:07.064081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:10.020808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:13.173396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:15.698301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:18.476758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:21.108922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:23.877301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:26.913269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:29.647866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:32.994014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:04.899049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:07.263572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:10.293958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:13.418163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:15.958638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:18.728222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:21.265368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:24.174970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:27.113028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:29.898673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:33.323239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:05.056678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:07.557373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:10.883437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:13.635422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:16.233377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:18.954277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:21.440043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:24.510068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:27.441200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:30.154785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:33.590295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:05.200140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:07.870784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:11.160373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:13.790988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:16.496152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:19.120368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:21.686176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:24.809339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:27.715487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:30.408225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:33.869678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:05.390311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:08.139582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:11.462945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:13.959147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:16.800133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:19.379578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:21.857405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:25.139291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:28.003672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:30.674628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:34.138525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:05.634093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:08.435564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:11.786240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:14.163564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:17.025909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:19.627747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:22.030442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:25.444036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:28.215752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:30.932442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:34.404630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:05.796011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:08.630514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:11.990043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:14.429571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:17.253093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:19.899926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:22.402961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:25.744956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:28.402316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:31.156260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:34.573233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:06.058446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:08.868001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:12.320945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:14.691263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:17.440640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:20.107511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:22.813122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:26.008920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:28.617568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:31.421976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:34.763680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:06.311890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:09.140548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:12.588063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:14.915216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:17.767185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:20.268021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:23.143235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:26.280931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:28.881033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:31.681148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:35.032464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:06.567600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:09.432633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:12.820727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:15.186479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:17.962332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:20.528881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:23.425071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:26.549386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:29.141919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:41:31.943899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:41:48.832787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치연도설치장소도로명주소설비용량(KW)설치비(백만원)2016년 발전량(kWh)2017년 발전량(kWh)2018년 발전량(kWh)2019년 발전량(kWh)2020년 발전량(kWh)2021년 발전량(kWh)2022년 발전량(kWh)
연번1.0000.9101.0001.0000.5710.0000.0870.4290.6450.6220.5760.6680.000
설치연도0.9101.0001.0001.0000.8530.7600.6780.7130.0000.0000.7550.5310.753
설치장소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설비용량(KW)0.5710.8531.0001.0001.0000.7680.2490.4100.6970.7020.7300.7520.988
설치비(백만원)0.0000.7601.0001.0000.7681.0000.3540.5030.6360.6010.7140.6020.730
2016년 발전량(kWh)0.0870.6781.0001.0000.2490.3541.0000.8960.8170.7910.5070.4650.210
2017년 발전량(kWh)0.4290.7131.0001.0000.4100.5030.8961.0000.9180.7650.8370.7960.384
2018년 발전량(kWh)0.6450.0001.0001.0000.6970.6360.8170.9181.0000.9610.9260.8440.634
2019년 발전량(kWh)0.6220.0001.0001.0000.7020.6010.7910.7650.9611.0000.9870.9530.704
2020년 발전량(kWh)0.5760.7551.0001.0000.7300.7140.5070.8370.9260.9871.0000.9780.744
2021년 발전량(kWh)0.6680.5311.0001.0000.7520.6020.4650.7960.8440.9530.9781.0000.726
2022년 발전량(kWh)0.0000.7531.0001.0000.9880.7300.2100.3840.6340.7040.7440.7261.000
2024-03-15T09:41:49.201984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치연도설비용량(KW)설치비(백만원)2016년 발전량(kWh)2017년 발전량(kWh)2018년 발전량(kWh)2019년 발전량(kWh)2020년 발전량(kWh)2021년 발전량(kWh)2022년 발전량(kWh)
연번1.0000.984-0.028-0.175-0.597-0.492-0.282-0.119-0.252-0.178-0.035
설치연도0.9841.000-0.014-0.147-0.616-0.506-0.270-0.109-0.269-0.177-0.040
설비용량(KW)-0.028-0.0141.0000.9430.3230.5740.7150.6160.7270.6460.965
설치비(백만원)-0.175-0.1470.9431.0000.3830.6200.6460.5210.6560.6110.911
2016년 발전량(kWh)-0.597-0.6160.3230.3831.0000.8560.5760.4970.4930.3610.304
2017년 발전량(kWh)-0.492-0.5060.5740.6200.8561.0000.8090.7400.7360.6160.533
2018년 발전량(kWh)-0.282-0.2700.7150.6460.5760.8091.0000.9330.8840.8150.674
2019년 발전량(kWh)-0.119-0.1090.6160.5210.4970.7400.9331.0000.8380.7850.589
2020년 발전량(kWh)-0.252-0.2690.7270.6560.4930.7360.8840.8381.0000.9030.722
2021년 발전량(kWh)-0.178-0.1770.6460.6110.3610.6160.8150.7850.9031.0000.672
2022년 발전량(kWh)-0.035-0.0400.9650.9110.3040.5330.6740.5890.7220.6721.000

Missing values

2024-03-15T09:41:35.429999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:41:36.062347image/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

연번단체명설치연도설치장소도로명주소설비용량(KW)설치비(백만원)2016년 발전량(kWh)2017년 발전량(kWh)2018년 발전량(kWh)2019년 발전량(kWh)2020년 발전량(kWh)2021년 발전량(kWh)2022년 발전량(kWh)데이터기준일자
01종로구2009평창동주민센터서울특별시 종로구 평창문화로 655.26559015510585059046040.0625363052024-01-05
12종로구2009삼청동주민센터서울특별시 종로구 삼청동 105110.0879784102131020098879468.874598977199822024-01-05
23종로구2009종로노인복지관(별관)서울특별시 종로구 이화동25110.0801014410683105711064410038.09793117032024-01-05
34종로구2009무악어린이집서울특별시 종로구 무악동 7910.07811314110178858894610929.01056111652024-01-05
45종로구2009청운어린이집서울특별시 종로구 청운동 635.04126433840476021436264.27572353372024-01-05
56종로구2009종로종합사회복지관서울특별시 종로구 창신동 2334410.078114341192378141079712660.010975117982024-01-05
67종로구2010종로구육아종합지원센터서울특별시 종로구 명륜3가 147110.087000011199.011896139002024-01-05
78종로구2011혜화동자치회관서울특별시 종로구 혜화동 74304.43155365048456043164671.0578759272024-01-05
89종로구2012종로장애인복지관 및 푸르메재활센터서울특별시 종로구 신교동 6633.0160238627675268552827628992.025450278082024-01-05
910종로구2014무악동노인복지센터(종로노인종합복지관 분관 무악센터)서울특별시 종로구 통일로14길 303.01300314834903170.018315317116222024-01-05
연번단체명설치연도설치장소도로명주소설비용량(KW)설치비(백만원)2016년 발전량(kWh)2017년 발전량(kWh)2018년 발전량(kWh)2019년 발전량(kWh)2020년 발전량(kWh)2021년 발전량(kWh)2022년 발전량(kWh)데이터기준일자
1314종로구2016무악동주민센터서울특별시 종로구 통일로14길 3632.4892641232839386603624737925.022232237222024-01-05
1415종로구2017종로노인종합복지관서울특별시 종로구 이화동 250132.09804908370103723234705.612934746258302024-01-05
1516종로구2017종로1234가동 청사서울특별시 종로구 삼일대로30길 4727.590010785262751674822825.021956194612024-01-05
1617종로구2017환경미화원휴게소서울특별시 종로구 신문로2가 1545.04400005146.098901498749252024-01-05
1718종로구2018삼청공원공중화장실1서울특별시 종로구 북촌로13434.01500028003448.0349823402024-01-05
1819종로구2018인왕산도시자연공원청운화장실서울특별시 종로구 청운동 7274.01400040793688.0235533312024-01-05
1920종로구2018와룡공영주차장서울특별시 종로구 명륜길 2818.25700205972157818263.020126202412024-01-05
2021종로구2018부암경로당서울특별시 종로구 자하문로 2603.46800036943437.0349835032024-01-05
2122종로구2018착한주차안내소서울특별시 종로구 율곡로19길 1783.46800046873915.0436544142024-01-05
2223종로구2021올림픽기념국민생활관서울특별시 종로구 성균관로 91160.032100000.001864252024-01-05