Overview

Dataset statistics

Number of variables4
Number of observations1372
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)0.9%
Total size in memory47.0 KiB
Average record size in memory35.1 B

Variable types

Categorical1
Numeric3

Dataset

Description서울특별시 광진구 관내의 공공시설 태양광 발전 설비에 대한 데이터를 제공합니다. 해당 데이터는 월별 발전량 현황을 제공합니다.
Author서울특별시 광진구
URLhttps://www.data.go.kr/data/15048668/fileData.do

Alerts

Dataset has 12 (0.9%) duplicate rowsDuplicates
발전량(KWh) has 91 (6.6%) zerosZeros

Reproduction

Analysis started2024-03-15 01:50:09.437090
Analysis finished2024-03-15 01:50:12.478351
Duration3.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Categorical

Distinct30
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
광진정보도서관 도서관동
 
85
자양공공힐링센터
 
78
구의3동주민센터
 
78
광진구행정차고지(60KW)
 
77
광진노인복지관
 
76
Other values (25)
978 

Length

Max length14
Median length11
Mean length8.6428571
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광진정보도서관 도서관동 85
 
6.2%
자양공공힐링센터 78
 
5.7%
구의3동주민센터 78
 
5.7%
광진구행정차고지(60KW) 77
 
5.6%
광진노인복지관 76
 
5.5%
광진구행정차고지(30KW) 72
 
5.2%
광진정보도서관 문화동 57
 
4.2%
능동경로당 51
 
3.7%
능동주민센터 50
 
3.6%
구의공원 47
 
3.4%
Other values (20) 701
51.1%

Length

2024-03-15T10:50:12.730260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광진정보도서관 142
 
9.3%
도서관동 85
 
5.6%
자양공공힐링센터 78
 
5.1%
구의3동주민센터 78
 
5.1%
광진구행정차고지(60kw 77
 
5.1%
광진노인복지관 76
 
5.0%
광진구행정차고지(30kw 72
 
4.7%
문화동 57
 
3.7%
능동경로당 51
 
3.3%
능동주민센터 50
 
3.3%
Other values (22) 757
49.7%

년
Real number (ℝ)

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.1108
Minimum2016
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-03-15T10:50:13.059107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2017
Q12020
median2021
Q32023
95-th percentile2023
Maximum2023
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7786234
Coefficient of variation (CV)0.00088002273
Kurtosis0.40776256
Mean2021.1108
Median Absolute Deviation (MAD)1
Skewness-1.0112218
Sum2772964
Variance3.1635013
MonotonicityNot monotonic
2024-03-15T10:50:13.429324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2023 348
25.4%
2022 336
24.5%
2021 282
20.6%
2020 197
14.4%
2018 72
 
5.2%
2017 59
 
4.3%
2019 53
 
3.9%
2016 25
 
1.8%
ValueCountFrequency (%)
2016 25
 
1.8%
2017 59
 
4.3%
2018 72
 
5.2%
2019 53
 
3.9%
2020 197
14.4%
2021 282
20.6%
2022 336
24.5%
2023 348
25.4%
ValueCountFrequency (%)
2023 348
25.4%
2022 336
24.5%
2021 282
20.6%
2020 197
14.4%
2019 53
 
3.9%
2018 72
 
5.2%
2017 59
 
4.3%
2016 25
 
1.8%

월
Real number (ℝ)

Distinct12
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6683673
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-03-15T10:50:13.803194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.451879
Coefficient of variation (CV)0.51764979
Kurtosis-1.2184867
Mean6.6683673
Median Absolute Deviation (MAD)3
Skewness-0.049516875
Sum9149
Variance11.915468
MonotonicityNot monotonic
2024-03-15T10:50:14.169363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 124
9.0%
12 124
9.0%
10 121
8.8%
4 114
8.3%
6 114
8.3%
7 114
8.3%
8 114
8.3%
9 114
8.3%
5 113
8.2%
3 109
7.9%
Other values (2) 211
15.4%
ValueCountFrequency (%)
1 103
7.5%
2 108
7.9%
3 109
7.9%
4 114
8.3%
5 113
8.2%
6 114
8.3%
7 114
8.3%
8 114
8.3%
9 114
8.3%
10 121
8.8%
ValueCountFrequency (%)
12 124
9.0%
11 124
9.0%
10 121
8.8%
9 114
8.3%
8 114
8.3%
7 114
8.3%
6 114
8.3%
5 113
8.2%
4 114
8.3%
3 109
7.9%

발전량(KWh)
Real number (ℝ)

ZEROS 

Distinct1025
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1320.0428
Minimum0
Maximum8056
Zeros91
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-03-15T10:50:14.685883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1402
median908
Q31748.5
95-th percentile3954.3
Maximum8056
Range8056
Interquartile range (IQR)1346.5

Descriptive statistics

Standard deviation1343.909
Coefficient of variation (CV)1.0180798
Kurtosis4.4059461
Mean1320.0428
Median Absolute Deviation (MAD)591
Skewness1.9073099
Sum1811098.7
Variance1806091.3
MonotonicityNot monotonic
2024-03-15T10:50:15.205926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 91
 
6.6%
1.0 11
 
0.8%
2562.0 9
 
0.7%
2479.0 6
 
0.4%
471.0 5
 
0.4%
1068.0 4
 
0.3%
317.0 4
 
0.3%
345.0 4
 
0.3%
269.0 4
 
0.3%
559.0 4
 
0.3%
Other values (1015) 1230
89.7%
ValueCountFrequency (%)
0.0 91
6.6%
0.71 1
 
0.1%
1.0 11
 
0.8%
3.25 1
 
0.1%
21.72 1
 
0.1%
30.05 1
 
0.1%
37.0 1
 
0.1%
39.0 1
 
0.1%
41.0 1
 
0.1%
55.0 1
 
0.1%
ValueCountFrequency (%)
8056.0 1
0.1%
8053.0 1
0.1%
7734.0 1
0.1%
7721.0 1
0.1%
7438.0 1
0.1%
7318.0 1
0.1%
7285.0 1
0.1%
7077.0 2
0.1%
6863.0 1
0.1%
6840.0 1
0.1%

Interactions

2024-03-15T10:50:11.161844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:50:09.698621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:50:10.454314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:50:11.447536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:50:09.993614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:50:10.624747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:50:11.711575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:50:10.184868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:50:10.851492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:50:15.391325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명년월발전량(KWh)
시설명1.0000.5240.0000.834
년0.5241.0000.0000.360
월0.0000.0001.0000.278
발전량(KWh)0.8340.3600.2781.000
2024-03-15T10:50:15.618165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월발전량(KWh)시설명
년1.000-0.057-0.2640.322
월-0.0571.000-0.1080.000
발전량(KWh)-0.264-0.1081.0000.430
시설명0.3220.0000.4301.000

Missing values

2024-03-15T10:50:12.078623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:50:12.367223image/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

시설명년월발전량(KWh)
0광진구행정차고지201611784.0
1광진구행정차고지201622301.0
2광진구행정차고지201633323.0
3광진구행정차고지201643530.0
4광진구행정차고지201654031.0
5광진구행정차고지201663374.0
6광진구행정차고지201672553.0
7광진구행정차고지201682918.0
8광진구행정차고지(30KW)201692748.0
9광진구행정차고지(30KW)2016102387.0
시설명년월발전량(KWh)
1362광진정보도서관 문화동20233614.0
1363광진정보도서관 문화동20234541.0
1364광진정보도서관 문화동20235629.0
1365광진정보도서관 문화동20236559.0
1366광진정보도서관 문화동20237471.0
1367광진정보도서관 문화동20238499.0
1368광진정보도서관 문화동20239481.0
1369광진정보도서관 문화동202310533.0
1370광진정보도서관 문화동202311421.0
1371광진정보도서관 문화동202312345.0

Duplicate rows

Most frequently occurring

시설명년월발전량(KWh)# duplicates
0광진정보도서관 문화동20231445.02
1광진정보도서관 문화동20232480.02
2광진정보도서관 문화동20233614.02
3광진정보도서관 문화동20234541.02
4광진정보도서관 문화동20235629.02
5광진정보도서관 문화동20236559.02
6광진정보도서관 문화동20237471.02
7광진정보도서관 문화동20238499.02
8광진정보도서관 문화동20239481.02
9광진정보도서관 문화동202310533.02