Overview

Dataset statistics

Number of variables11
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory99.1 B

Variable types

Numeric6
Text2
DateTime1
Categorical2

Dataset

Description서울특별시 영등포구 공공건축의 태양광 발전기 현황을 제공합니다.시설명, 소재지, 설치일자, 설비용량, 발전유형, 설치면적, 건축물 연면적, 층수, 연간 전력 발전량을 제공합니다.
Author서울특별시 영등포구
URLhttps://www.data.go.kr/data/15126207/fileData.do

Alerts

발전유형 has constant value ""Constant
설비용량(kWh) is highly overall correlated with 설치면적(제곱미터) and 2 other fieldsHigh correlation
설치면적(제곱미터) is highly overall correlated with 설비용량(kWh) and 2 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 설비용량(kWh) and 4 other fieldsHigh correlation
지상층수 is highly overall correlated with 연면적(제곱미터)High correlation
연간발전량(kWh)(2022년) is highly overall correlated with 설비용량(kWh) and 2 other fieldsHigh correlation
지하층수 is highly overall correlated with 연면적(제곱미터)High correlation
연번 has unique valuesUnique
시설명 has unique valuesUnique
연간발전량(kWh)(2022년) has unique valuesUnique

Reproduction

Analysis started2024-01-14 13:49:10.518165
Analysis finished2024-01-14 13:49:15.962032
Duration5.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.5
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-14T22:49:16.098135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.55
Q18.75
median16.5
Q324.25
95-th percentile30.45
Maximum32
Range31
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.3808315
Coefficient of variation (CV)0.56853524
Kurtosis-1.2
Mean16.5
Median Absolute Deviation (MAD)8
Skewness0
Sum528
Variance88
MonotonicityStrictly increasing
2024-01-14T22:49:16.333339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 1
 
3.1%
18 1
 
3.1%
32 1
 
3.1%
31 1
 
3.1%
30 1
 
3.1%
29 1
 
3.1%
28 1
 
3.1%
27 1
 
3.1%
26 1
 
3.1%
25 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1 1
3.1%
2 1
3.1%
3 1
3.1%
4 1
3.1%
5 1
3.1%
6 1
3.1%
7 1
3.1%
8 1
3.1%
9 1
3.1%
10 1
3.1%
ValueCountFrequency (%)
32 1
3.1%
31 1
3.1%
30 1
3.1%
29 1
3.1%
28 1
3.1%
27 1
3.1%
26 1
3.1%
25 1
3.1%
24 1
3.1%
23 1
3.1%

시설명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-01-14T22:49:16.646931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.8125
Min length6

Characters and Unicode

Total characters282
Distinct characters87
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row신길종합사회복지관
2nd row영등포청소년문화의집(가동)
3rd row대림1동주민센터
4th row영등포보건분소
5th row영등포그린&케어센터
ValueCountFrequency (%)
신길종합사회복지관 1
 
2.9%
양평1빗물펌프장 1
 
2.9%
취업정보센터 1
 
2.9%
영등포구제1스포츠센터 1
 
2.9%
영등포구청사 1
 
2.9%
문래빗물펌프장 1
 
2.9%
문래청소년수련관 1
 
2.9%
영등포구청제2스포츠센터 1
 
2.9%
도림2빗물펌프장 1
 
2.9%
신길4동 1
 
2.9%
Other values (25) 25
71.4%
2024-01-14T22:49:17.096438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
포 13
 
4.6%
센 12
 
4.3%
터 12
 
4.3%
영 11
 
3.9%
등 11
 
3.9%
동 11
 
3.9%
관 8
 
2.8%
청 7
 
2.5%
민 7
 
2.5%
구 6
 
2.1%
Other values (77) 184
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 259
91.8%
Decimal Number 15
 
5.3%
Space Separator 3
 
1.1%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
포 13
 
5.0%
센 12
 
4.6%
터 12
 
4.6%
영 11
 
4.2%
등 11
 
4.2%
동 11
 
4.2%
관 8
 
3.1%
청 7
 
2.7%
민 7
 
2.7%
구 6
 
2.3%
Other values (68) 161
62.2%
Decimal Number
ValueCountFrequency (%)
1 5
33.3%
2 5
33.3%
3 2
 
13.3%
4 2
 
13.3%
5 1
 
6.7%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 259
91.8%
Common 23
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
포 13
 
5.0%
센 12
 
4.6%
터 12
 
4.6%
영 11
 
4.2%
등 11
 
4.2%
동 11
 
4.2%
관 8
 
3.1%
청 7
 
2.7%
민 7
 
2.7%
구 6
 
2.3%
Other values (68) 161
62.2%
Common
ValueCountFrequency (%)
1 5
21.7%
2 5
21.7%
3
13.0%
3 2
 
8.7%
4 2
 
8.7%
( 2
 
8.7%
) 2
 
8.7%
5 1
 
4.3%
& 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 259
91.8%
ASCII 23
 
8.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
포 13
 
5.0%
센 12
 
4.6%
터 12
 
4.6%
영 11
 
4.2%
등 11
 
4.2%
동 11
 
4.2%
관 8
 
3.1%
청 7
 
2.7%
민 7
 
2.7%
구 6
 
2.3%
Other values (68) 161
62.2%
ASCII
ValueCountFrequency (%)
1 5
21.7%
2 5
21.7%
3
13.0%
3 2
 
8.7%
4 2
 
8.7%
( 2
 
8.7%
) 2
 
8.7%
5 1
 
4.3%
& 1
 
4.3%

소재지
Text

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-01-14T22:49:17.394019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20.5
Mean length19.65625
Min length16

Characters and Unicode

Total characters629
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)81.2%

Sample

1st row서울특별시 영등포구 영등포로84길 24-5
2nd row서울특별시 영등포구 영등포로64길 15
3rd row서울특별시 영등포구 디지털로 441
4th row서울특별시 영등포구 디지털로 441
5th row서울특별시 영등포구 당산로29길 9
ValueCountFrequency (%)
서울특별시 32
25.4%
영등포구 32
25.4%
디지털로 2
 
1.6%
도림로 2
 
1.6%
441 2
 
1.6%
1 2
 
1.6%
13 2
 
1.6%
영등포로29길 2
 
1.6%
123 2
 
1.6%
당산로 2
 
1.6%
Other values (46) 46
36.5%
2024-01-14T22:49:17.834077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
94
14.9%
영 38
 
6.0%
등 38
 
6.0%
포 38
 
6.0%
서 32
 
5.1%
특 32
 
5.1%
별 32
 
5.1%
시 32
 
5.1%
구 32
 
5.1%
울 32
 
5.1%
Other values (38) 229
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 416
66.1%
Decimal Number 112
 
17.8%
Space Separator 94
 
14.9%
Dash Punctuation 7
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 38
9.1%
등 38
9.1%
포 38
9.1%
서 32
 
7.7%
특 32
 
7.7%
별 32
 
7.7%
시 32
 
7.7%
구 32
 
7.7%
울 32
 
7.7%
로 29
 
7.0%
Other values (26) 81
19.5%
Decimal Number
ValueCountFrequency (%)
1 26
23.2%
2 20
17.9%
4 18
16.1%
3 12
10.7%
5 9
 
8.0%
8 7
 
6.2%
9 6
 
5.4%
0 6
 
5.4%
7 5
 
4.5%
6 3
 
2.7%
Space Separator
ValueCountFrequency (%)
94
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 416
66.1%
Common 213
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 38
9.1%
등 38
9.1%
포 38
9.1%
서 32
 
7.7%
특 32
 
7.7%
별 32
 
7.7%
시 32
 
7.7%
구 32
 
7.7%
울 32
 
7.7%
로 29
 
7.0%
Other values (26) 81
19.5%
Common
ValueCountFrequency (%)
94
44.1%
1 26
 
12.2%
2 20
 
9.4%
4 18
 
8.5%
3 12
 
5.6%
5 9
 
4.2%
- 7
 
3.3%
8 7
 
3.3%
9 6
 
2.8%
0 6
 
2.8%
Other values (2) 8
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 416
66.1%
ASCII 213
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
94
44.1%
1 26
 
12.2%
2 20
 
9.4%
4 18
 
8.5%
3 12
 
5.6%
5 9
 
4.2%
- 7
 
3.3%
8 7
 
3.3%
9 6
 
2.8%
0 6
 
2.8%
Other values (2) 8
 
3.8%
Hangul
ValueCountFrequency (%)
영 38
9.1%
등 38
9.1%
포 38
9.1%
서 32
 
7.7%
특 32
 
7.7%
별 32
 
7.7%
시 32
 
7.7%
구 32
 
7.7%
울 32
 
7.7%
로 29
 
7.0%
Other values (26) 81
19.5%

설치일
Date

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum2008-01-31 00:00:00
Maximum2020-09-29 00:00:00
2024-01-14T22:49:17.986842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:18.495340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

설비용량(kWh)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.9375
Minimum3
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-14T22:49:18.698101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q18.25
median11.5
Q325.25
95-th percentile44.5
Maximum61
Range58
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.318807
Coefficient of variation (CV)0.8453908
Kurtosis1.9965803
Mean16.9375
Median Absolute Deviation (MAD)7.5
Skewness1.456622
Sum542
Variance205.02823
MonotonicityNot monotonic
2024-01-14T22:49:18.873519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10 6
18.8%
3 6
18.8%
15 4
12.5%
28 1
 
3.1%
12 1
 
3.1%
20 1
 
3.1%
50 1
 
3.1%
33 1
 
3.1%
40 1
 
3.1%
30 1
 
3.1%
Other values (9) 9
28.1%
ValueCountFrequency (%)
3 6
18.8%
5 1
 
3.1%
6 1
 
3.1%
9 1
 
3.1%
10 6
18.8%
11 1
 
3.1%
12 1
 
3.1%
15 4
12.5%
16 1
 
3.1%
20 1
 
3.1%
ValueCountFrequency (%)
61 1
3.1%
50 1
3.1%
40 1
3.1%
33 1
3.1%
32 1
3.1%
30 1
3.1%
28 1
3.1%
26 1
3.1%
25 1
3.1%
20 1
3.1%

발전유형
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
태양광발전
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
태양광발전 32
100.0%

Length

2024-01-14T22:49:19.052458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:49:19.192437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
태양광발전 32
100.0%

설치면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.10031
Minimum18.42
Maximum375.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-14T22:49:19.328198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.42
5-th percentile18.42
Q150.0325
median69.975
Q3155.6075
95-th percentile273.161
Maximum375.67
Range357.25
Interquartile range (IQR)105.575

Descriptive statistics

Standard deviation88.234872
Coefficient of variation (CV)0.84759469
Kurtosis1.9770592
Mean104.10031
Median Absolute Deviation (MAD)45.42
Skewness1.451182
Sum3331.21
Variance7785.3927
MonotonicityNot monotonic
2024-01-14T22:49:19.493890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
61.38 6
18.8%
18.42 6
18.8%
92.08 4
12.5%
171.88 1
 
3.1%
73.66 1
 
3.1%
123.75 1
 
3.1%
306.92 1
 
3.1%
202.57 1
 
3.1%
245.54 1
 
3.1%
184.15 1
 
3.1%
Other values (9) 9
28.1%
ValueCountFrequency (%)
18.42 6
18.8%
30.69 1
 
3.1%
34.38 1
 
3.1%
55.25 1
 
3.1%
61.38 6
18.8%
66.29 1
 
3.1%
73.66 1
 
3.1%
92.08 4
12.5%
98.95 1
 
3.1%
123.75 1
 
3.1%
ValueCountFrequency (%)
375.67 1
3.1%
306.92 1
3.1%
245.54 1
3.1%
202.57 1
3.1%
198.88 1
3.1%
184.15 1
3.1%
171.88 1
3.1%
162.05 1
3.1%
153.46 1
3.1%
123.75 1
3.1%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2755.4097
Minimum279.88
Maximum9682.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-14T22:49:19.686678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum279.88
5-th percentile355.992
Q1738.53
median1647.22
Q33908.7575
95-th percentile7992.2225
Maximum9682.5
Range9402.62
Interquartile range (IQR)3170.2275

Descriptive statistics

Standard deviation2677.083
Coefficient of variation (CV)0.97157347
Kurtosis0.63278636
Mean2755.4097
Median Absolute Deviation (MAD)1168.495
Skewness1.2601862
Sum88173.11
Variance7166773.1
MonotonicityNot monotonic
2024-01-14T22:49:19.985337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
738.53 2
 
6.2%
6477.57 1
 
3.1%
399.84 1
 
3.1%
279.88 1
 
3.1%
302.4 1
 
3.1%
400.8 1
 
3.1%
1261.35 1
 
3.1%
985.5 1
 
3.1%
682.22 1
 
3.1%
1043.49 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
279.88 1
3.1%
302.4 1
3.1%
399.84 1
3.1%
400.8 1
3.1%
482.64 1
3.1%
670.16 1
3.1%
682.22 1
3.1%
738.53 2
6.2%
976.0 1
3.1%
985.5 1
3.1%
ValueCountFrequency (%)
9682.5 1
3.1%
9169.36 1
3.1%
7029.11 1
3.1%
6674.19 1
3.1%
6477.57 1
3.1%
5610.63 1
3.1%
5549.67 1
3.1%
4443.8 1
3.1%
3730.41 1
3.1%
2999.18 1
3.1%

지하층수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
1
15 
<NA>
8 
2
7 
3
2 

Length

Max length4
Median length1
Mean length1.75
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 15
46.9%
<NA> 8
25.0%
2 7
21.9%
3 2
 
6.2%

Length

2024-01-14T22:49:20.190258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:49:20.343360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 15
46.9%
na 8
25.0%
2 7
21.9%
3 2
 
6.2%

지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.65625
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-14T22:49:20.468064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.55
Q13
median3.5
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3821296
Coefficient of variation (CV)0.37801835
Kurtosis-0.68554487
Mean3.65625
Median Absolute Deviation (MAD)1
Skewness-0.034286121
Sum117
Variance1.9102823
MonotonicityNot monotonic
2024-01-14T22:49:20.580463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 10
31.2%
5 7
21.9%
4 6
18.8%
2 4
 
12.5%
6 3
 
9.4%
1 2
 
6.2%
ValueCountFrequency (%)
1 2
 
6.2%
2 4
 
12.5%
3 10
31.2%
4 6
18.8%
5 7
21.9%
6 3
 
9.4%
ValueCountFrequency (%)
6 3
 
9.4%
5 7
21.9%
4 6
18.8%
3 10
31.2%
2 4
 
12.5%
1 2
 
6.2%

연간발전량(kWh)(2022년)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16658.469
Minimum2020
Maximum59872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-14T22:49:20.708021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2020
5-th percentile2748.4
Q15085
median12060.5
Q320957.75
95-th percentile45361.1
Maximum59872
Range57852
Interquartile range (IQR)15872.75

Descriptive statistics

Standard deviation14842.236
Coefficient of variation (CV)0.89097241
Kurtosis1.410109
Mean16658.469
Median Absolute Deviation (MAD)7753
Skewness1.3811165
Sum533071
Variance2.2029197 × 108
MonotonicityNot monotonic
2024-01-14T22:49:20.849000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
50310 1
 
3.1%
3504 1
 
3.1%
3954 1
 
3.1%
3311 1
 
3.1%
2797 1
 
3.1%
2020 1
 
3.1%
12229 1
 
3.1%
10521 1
 
3.1%
18567 1
 
3.1%
22166 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
2020 1
3.1%
2689 1
3.1%
2797 1
3.1%
3311 1
3.1%
3504 1
3.1%
3954 1
3.1%
4118 1
3.1%
4497 1
3.1%
5281 1
3.1%
6253 1
3.1%
ValueCountFrequency (%)
59872 1
3.1%
50310 1
3.1%
41312 1
3.1%
37342 1
3.1%
37078 1
3.1%
29832 1
3.1%
28806 1
3.1%
22166 1
3.1%
20555 1
3.1%
18567 1
3.1%

Interactions

2024-01-14T22:49:14.697414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.053840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.740116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:12.499080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:13.364663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:14.029074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:14.834903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.177985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.851114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:12.637931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:13.472270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:14.150615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:14.963869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.295927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.980778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:12.757981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:13.585701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:14.271453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:15.110845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.413505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:12.123112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:12.942691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:13.699179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:14.388720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:15.238260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.524305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:12.262292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:13.063877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:13.821604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:14.483507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:15.361472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:11.621645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:12.377217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:13.214611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:13.930341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:49:14.583679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T22:49:20.994288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설명소재지설치일설비용량(kWh)설치면적(제곱미터)연면적(제곱미터)지하층수지상층수연간발전량(kWh)(2022년)
연번1.0001.0000.9270.9610.5040.4890.0000.7010.4670.509
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지0.9271.0001.0000.9780.9160.9160.9751.0000.9550.960
설치일0.9611.0000.9781.0000.9320.9320.9640.9260.6400.929
설비용량(kWh)0.5041.0000.9160.9321.0000.9990.8000.3900.3950.863
설치면적(제곱미터)0.4891.0000.9160.9320.9991.0000.7450.5020.4760.869
연면적(제곱미터)0.0001.0000.9750.9640.8000.7451.0000.9700.0000.527
지하층수0.7011.0001.0000.9260.3900.5020.9701.0000.0000.764
지상층수0.4671.0000.9550.6400.3950.4760.0000.0001.0000.390
연간발전량(kWh)(2022년)0.5091.0000.9600.9290.8630.8690.5270.7640.3901.000
2024-01-14T22:49:21.181330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설비용량(kWh)설치면적(제곱미터)연면적(제곱미터)지상층수연간발전량(kWh)(2022년)지하층수
연번1.000-0.279-0.279-0.335-0.349-0.1790.443
설비용량(kWh)-0.2791.0001.0000.627-0.0190.8550.366
설치면적(제곱미터)-0.2791.0001.0000.627-0.0190.8550.335
연면적(제곱미터)-0.3350.6270.6271.0000.5350.5710.672
지상층수-0.349-0.019-0.0190.5351.000-0.0920.000
연간발전량(kWh)(2022년)-0.1790.8550.8550.571-0.0921.0000.400
지하층수0.4430.3660.3350.6720.0000.4001.000

Missing values

2024-01-14T22:49:15.578689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T22:49:15.859844image/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

연번시설명소재지설치일설비용량(kWh)발전유형설치면적(제곱미터)연면적(제곱미터)지하층수지상층수연간발전량(kWh)(2022년)
01신길종합사회복지관서울특별시 영등포구 영등포로84길 24-52009-04-1528태양광발전171.886477.572450310
12영등포청소년문화의집(가동)서울특별시 영등포구 영등포로64길 152009-06-0325태양광발전153.461498.01312139
23대림1동주민센터서울특별시 영등포구 디지털로 4412010-06-1610태양광발전61.38670.16132689
34영등포보건분소서울특별시 영등포구 디지털로 4412010-06-1610태양광발전61.381200.61311493
45영등포그린&케어센터서울특별시 영등포구 당산로29길 92012-02-235태양광발전30.692819.63266253
56문래정보문화도서관서울특별시 영등포구 문래로20길 492015-06-3015태양광발전92.081796.441515486
67선유정보문화도서관서울특별시 영등포구 선유로43가길 10-82015-07-1011태양광발전66.292339.93156314
78당산2동 주민센터서울특별시 영등포구 당산로41길 72015-08-1016태양광발전98.952992.792518556
89신길3동주민센터서울특별시 영등포구 신길로41라길 13-82015-10-2015태양광발전92.081046.31415996
910여의도복지센터서울특별시 영등포구 여의대방로 3722015-12-3026태양광발전162.053730.412428806
연번시설명소재지설치일설비용량(kWh)발전유형설치면적(제곱미터)연면적(제곱미터)지하층수지상층수연간발전량(kWh)(2022년)
2223영등포구청제2스포츠센터서울특별시 영등포구 국회대로 6152014-06-0933태양광발전202.576674.193537342
2324양평1빗물펌프장서울특별시 영등포구 양평동3가 57-32015-10-1250태양광발전306.921845.25<NA>241312
2425도림2빗물펌프장서울특별시 영등포구 도산로15길 112018-06-2820태양광발전123.751043.49<NA>222166
2526양평2빗물펌프장서울특별시 영등포구 양평동 35-22018-11-1915태양광발전92.08682.221118567
2627영등포동주민센터서울특별시 영등포구 영등포로53길 222012-01-0510태양광발전61.38985.51210521
2728신길5동주민센터서울특별시 영등포구 도림로 2642012-01-0510태양광발전61.381261.351312229
2829양평4가경로당서울특별시 영등포구 양평로20길14-12016-08-193태양광발전18.42400.8<NA>32020
2930양평1동경로당서울특별시 영등포구 영등포로12길 82016-08-193태양광발전18.42302.4132797
3031대림3동경로당서울특별시 영등포구 대림로41길122016-08-193태양광발전18.42279.88133311
3132당산1가경로당서울특별시 영등포구 영등포로29길 132016-10-313태양광발전18.42738.53<NA>53954