Overview

Dataset statistics

Number of variables6
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory55.4 B

Variable types

DateTime1
Numeric5

Dataset

Description한국지역난방공사가 보유한 태양광발전소(판교, 함백, 광양함만 1~3자전거도로)의 발전량에 대한 데이터로 실적일자, 각발전소별 발전량을 제공합니다.
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15044220/fileData.do

Alerts

함백 is highly overall correlated with 광양항만제1자전거도로 and 2 other fieldsHigh correlation
광양항만제1자전거도로 is highly overall correlated with 함백 and 2 other fieldsHigh correlation
광양항만제2자전거도로 is highly overall correlated with 함백 and 2 other fieldsHigh correlation
광양항만제3자전거도로 is highly overall correlated with 함백 and 2 other fieldsHigh correlation
실적일자 has unique valuesUnique
판교 has unique valuesUnique
판교 has 1 (1.8%) zerosZeros
함백 has 27 (48.2%) zerosZeros
광양항만제1자전거도로 has 37 (66.1%) zerosZeros
광양항만제2자전거도로 has 37 (66.1%) zerosZeros
광양항만제3자전거도로 has 37 (66.1%) zerosZeros

Reproduction

Analysis started2023-12-12 10:11:52.762167
Analysis finished2023-12-12 10:11:56.134048
Duration3.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

실적일자
Date

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
Minimum2018-01-31 00:00:00
Maximum2022-08-31 00:00:00
2023-12-12T19:11:56.533141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:56.712627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

판교
Real number (ℝ)

UNIQUE  ZEROS 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4901.5357
Minimum0
Maximum7711
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T19:11:56.868598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2773.5
Q14008.25
median4967.5
Q36112.75
95-th percentile7001.5
Maximum7711
Range7711
Interquartile range (IQR)2104.5

Descriptive statistics

Standard deviation1432.4869
Coefficient of variation (CV)0.29225268
Kurtosis1.0201027
Mean4901.5357
Median Absolute Deviation (MAD)1056
Skewness-0.57597034
Sum274486
Variance2052018.8
MonotonicityNot monotonic
2023-12-12T19:11:57.041350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2582 1
 
1.8%
6124 1
 
1.8%
3682 1
 
1.8%
5029 1
 
1.8%
5067 1
 
1.8%
3528 1
 
1.8%
3541 1
 
1.8%
2791 1
 
1.8%
4030 1
 
1.8%
5292 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
0 1
1.8%
2582 1
1.8%
2721 1
1.8%
2791 1
1.8%
3147 1
1.8%
3235 1
1.8%
3432 1
1.8%
3528 1
1.8%
3541 1
1.8%
3616 1
1.8%
ValueCountFrequency (%)
7711 1
1.8%
7173 1
1.8%
7021 1
1.8%
6995 1
1.8%
6664 1
1.8%
6569 1
1.8%
6412 1
1.8%
6400 1
1.8%
6385 1
1.8%
6250 1
1.8%

함백
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58626.268
Minimum0
Maximum166932
Zeros27
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T19:11:57.189731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median66542.5
Q3112648
95-th percentile145097.75
Maximum166932
Range166932
Interquartile range (IQR)112648

Descriptive statistics

Standard deviation59905.803
Coefficient of variation (CV)1.0218253
Kurtosis-1.6970831
Mean58626.268
Median Absolute Deviation (MAD)66542.5
Skewness0.20075046
Sum3283071
Variance3.5887052 × 109
MonotonicityNot monotonic
2023-12-12T19:11:57.315519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 27
48.2%
145015 1
 
1.8%
87790 1
 
1.8%
124697 1
 
1.8%
125402 1
 
1.8%
166932 1
 
1.8%
153302 1
 
1.8%
119038 1
 
1.8%
118750 1
 
1.8%
101693 1
 
1.8%
Other values (20) 20
35.7%
ValueCountFrequency (%)
0 27
48.2%
66190 1
 
1.8%
66895 1
 
1.8%
83131 1
 
1.8%
84982 1
 
1.8%
87552 1
 
1.8%
87790 1
 
1.8%
93809 1
 
1.8%
94241 1
 
1.8%
98482 1
 
1.8%
ValueCountFrequency (%)
166932 1
1.8%
153302 1
1.8%
145346 1
1.8%
145015 1
1.8%
141667 1
1.8%
140256 1
1.8%
131760 1
1.8%
129974 1
1.8%
126374 1
1.8%
125402 1
1.8%

광양항만제1자전거도로
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18067.75
Minimum0
Maximum69694
Zeros37
Zeros (%)66.1%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T19:11:57.425500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q345951.5
95-th percentile61726.5
Maximum69694
Range69694
Interquartile range (IQR)45951.5

Descriptive statistics

Standard deviation26007.212
Coefficient of variation (CV)1.4394273
Kurtosis-1.1990119
Mean18067.75
Median Absolute Deviation (MAD)0
Skewness0.82818068
Sum1011794
Variance6.7637507 × 108
MonotonicityNot monotonic
2023-12-12T19:11:57.553407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 37
66.1%
44872 1
 
1.8%
39376 1
 
1.8%
55392 1
 
1.8%
52864 1
 
1.8%
69694 1
 
1.8%
62460 1
 
1.8%
54826 1
 
1.8%
50886 1
 
1.8%
49190 1
 
1.8%
Other values (10) 10
 
17.9%
ValueCountFrequency (%)
0 37
66.1%
33486 1
 
1.8%
39376 1
 
1.8%
40330 1
 
1.8%
44724 1
 
1.8%
44872 1
 
1.8%
49190 1
 
1.8%
50886 1
 
1.8%
52864 1
 
1.8%
53368 1
 
1.8%
ValueCountFrequency (%)
69694 1
1.8%
64966 1
1.8%
62460 1
1.8%
61482 1
1.8%
60638 1
1.8%
59146 1
1.8%
57514 1
1.8%
56580 1
1.8%
55392 1
1.8%
54826 1
1.8%

광양항만제2자전거도로
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19119.286
Minimum0
Maximum75580
Zeros37
Zeros (%)66.1%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T19:11:57.673620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q347855
95-th percentile66044
Maximum75580
Range75580
Interquartile range (IQR)47855

Descriptive statistics

Standard deviation27714.707
Coefficient of variation (CV)1.4495681
Kurtosis-1.1280706
Mean19119.286
Median Absolute Deviation (MAD)0
Skewness0.85843368
Sum1070680
Variance7.6810501 × 108
MonotonicityNot monotonic
2023-12-12T19:11:57.807523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 37
66.1%
46204 1
 
1.8%
41590 1
 
1.8%
58864 1
 
1.8%
55656 1
 
1.8%
75580 1
 
1.8%
67838 1
 
1.8%
60504 1
 
1.8%
56554 1
 
1.8%
52808 1
 
1.8%
Other values (10) 10
 
17.9%
ValueCountFrequency (%)
0 37
66.1%
27640 1
 
1.8%
41590 1
 
1.8%
42626 1
 
1.8%
45876 1
 
1.8%
46204 1
 
1.8%
52808 1
 
1.8%
55656 1
 
1.8%
56554 1
 
1.8%
57048 1
 
1.8%
ValueCountFrequency (%)
75580 1
1.8%
69604 1
1.8%
67838 1
1.8%
65446 1
1.8%
64396 1
1.8%
63256 1
1.8%
61594 1
1.8%
60504 1
1.8%
58864 1
1.8%
57596 1
1.8%

광양항만제3자전거도로
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10428.911
Minimum0
Maximum41198
Zeros37
Zeros (%)66.1%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T19:11:57.931667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324886
95-th percentile36313
Maximum41198
Range41198
Interquartile range (IQR)24886

Descriptive statistics

Standard deviation15067.559
Coefficient of variation (CV)1.4447874
Kurtosis-1.1282677
Mean10428.911
Median Absolute Deviation (MAD)0
Skewness0.85123404
Sum584019
Variance2.2703132 × 108
MonotonicityNot monotonic
2023-12-12T19:11:58.040906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 37
66.1%
23919 1
 
1.8%
22796 1
 
1.8%
32767 1
 
1.8%
31165 1
 
1.8%
41198 1
 
1.8%
37024 1
 
1.8%
32666 1
 
1.8%
29962 1
 
1.8%
27787 1
 
1.8%
Other values (10) 10
 
17.9%
ValueCountFrequency (%)
0 37
66.1%
19612 1
 
1.8%
22796 1
 
1.8%
23182 1
 
1.8%
23823 1
 
1.8%
23919 1
 
1.8%
27787 1
 
1.8%
29962 1
 
1.8%
30502 1
 
1.8%
30509 1
 
1.8%
ValueCountFrequency (%)
41198 1
1.8%
38066 1
1.8%
37024 1
1.8%
36076 1
1.8%
34668 1
1.8%
34588 1
1.8%
33709 1
1.8%
32767 1
1.8%
32666 1
1.8%
31165 1
1.8%

Interactions

2023-12-12T19:11:55.333813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:52.967633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:53.608628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.180847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.767267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:55.460302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:53.100027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:53.736009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.299780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.889175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:55.565037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:53.223937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:53.859283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.420391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.998768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:55.695891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:53.364702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:53.970524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.537111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:55.104789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:55.834269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:53.491641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.084234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:54.660386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:55.233823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:11:58.129475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실적일자판교함백광양항만제1자전거도로광양항만제2자전거도로광양항만제3자전거도로
실적일자1.0001.0001.0001.0001.0001.000
판교1.0001.0000.6180.0750.3120.233
함백1.0000.6181.0000.6480.6420.661
광양항만제1자전거도로1.0000.0750.6481.0000.9960.994
광양항만제2자전거도로1.0000.3120.6420.9961.0000.995
광양항만제3자전거도로1.0000.2330.6610.9940.9951.000
2023-12-12T19:11:58.235089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
판교함백광양항만제1자전거도로광양항만제2자전거도로광양항만제3자전거도로
판교1.000-0.017-0.049-0.047-0.046
함백-0.0171.0000.7560.7570.759
광양항만제1자전거도로-0.0490.7561.0001.0000.999
광양항만제2자전거도로-0.0470.7571.0001.0000.999
광양항만제3자전거도로-0.0460.7590.9990.9991.000

Missing values

2023-12-12T19:11:55.970313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:11:56.087576image/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

실적일자판교함백광양항만제1자전거도로광양항만제2자전거도로광양항만제3자전거도로
02018-01-3125820000
12018-02-2863850000
22018-03-3162500000
32018-04-3064000000
42018-05-3169950000
52018-06-3070210000
62018-07-3166640000
72018-08-3161780000
82018-09-3061540000
92018-10-3158470000
실적일자판교함백광양항만제1자전거도로광양항만제2자전거도로광양항만제3자전거도로
462021-11-30323583131447244587623182
472021-12-31314787552448724620423919
482022-01-313616101693491905280827787
492022-02-284192118750508865655429962
502022-03-314381119038548266050432666
512022-04-305971153302624606783837024
522022-05-316569166932696947558041198
532022-06-303880125402528645565631165
542022-07-315587124697553925886432767
552022-08-31394387790393764159022796