Overview

Dataset statistics

Number of variables2
Number of observations601
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory18.2 B

Variable types

Numeric2

Dataset

Description기본적으로 기온반응함수는 기온이 도시가스수요에 미치는 영향을 각 개별 기온대별로 나타내어 일종의 함수 형태로 제시한 것으로 기온분포가 도시가스 수요에 미친 영향을 측정한 것으로 해석할 수 있다. 기온반응함수는 일반적으로 기온에 대하여 비선형적 형태를 보이기에 개별 기온이 부산 산업용 도시가스 수요에 미치는 영향의 상대적 중요도를 측정할 수 있다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15088567/fileData.do

Alerts

기온(s) is highly overall correlated with 기온반응도(f(s))High correlation
기온반응도(f(s)) is highly overall correlated with 기온(s)High correlation
기온(s) has unique valuesUnique
기온반응도(f(s)) has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:24:22.581104
Analysis finished2023-12-12 12:24:23.228263
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기온(s)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct601
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10
Minimum-20
Maximum40
Zeros1
Zeros (%)0.2%
Negative200
Negative (%)33.3%
Memory size5.4 KiB
2023-12-12T21:24:23.330499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-20
5-th percentile-17
Q1-5
median10
Q325
95-th percentile37
Maximum40
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.363803
Coefficient of variation (CV)1.7363803
Kurtosis-1.2
Mean10
Median Absolute Deviation (MAD)15
Skewness-7.4370716 × 10-17
Sum6010
Variance301.50167
MonotonicityStrictly increasing
2023-12-12T21:24:23.492392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-20.0 1
 
0.2%
19.5 1
 
0.2%
19.7 1
 
0.2%
19.8 1
 
0.2%
19.9 1
 
0.2%
20.0 1
 
0.2%
20.1 1
 
0.2%
20.2 1
 
0.2%
20.3 1
 
0.2%
20.4 1
 
0.2%
Other values (591) 591
98.3%
ValueCountFrequency (%)
-20.0 1
0.2%
-19.9 1
0.2%
-19.8 1
0.2%
-19.7 1
0.2%
-19.6 1
0.2%
-19.5 1
0.2%
-19.4 1
0.2%
-19.3 1
0.2%
-19.2 1
0.2%
-19.1 1
0.2%
ValueCountFrequency (%)
40.0 1
0.2%
39.9 1
0.2%
39.8 1
0.2%
39.7 1
0.2%
39.6 1
0.2%
39.5 1
0.2%
39.4 1
0.2%
39.3 1
0.2%
39.2 1
0.2%
39.1 1
0.2%

기온반응도(f(s))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct601
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.327787 × 10-8
Minimum-0.50044
Maximum0.63091
Zeros0
Zeros (%)0.0%
Negative340
Negative (%)56.6%
Memory size5.4 KiB
2023-12-12T21:24:23.694362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.50044
5-th percentile-0.43653
Q1-0.24883
median-0.06545
Q30.24861
95-th percentile0.5606
Maximum0.63091
Range1.13135
Interquartile range (IQR)0.49744

Descriptive statistics

Standard deviation0.31246364
Coefficient of variation (CV)9389532.3
Kurtosis-0.96453836
Mean3.327787 × 10-8
Median Absolute Deviation (MAD)0.22822
Skewness0.40064957
Sum2 × 10-5
Variance0.097633524
MonotonicityStrictly decreasing
2023-12-12T21:24:23.911798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.63091 1
 
0.2%
-0.18621 1
 
0.2%
-0.18844 1
 
0.2%
-0.18956 1
 
0.2%
-0.19068 1
 
0.2%
-0.19179 1
 
0.2%
-0.1929 1
 
0.2%
-0.19402 1
 
0.2%
-0.19513 1
 
0.2%
-0.19625 1
 
0.2%
Other values (591) 591
98.3%
ValueCountFrequency (%)
-0.50044 1
0.2%
-0.4982 1
0.2%
-0.49597 1
0.2%
-0.49375 1
0.2%
-0.49153 1
0.2%
-0.48932 1
0.2%
-0.48711 1
0.2%
-0.48492 1
0.2%
-0.48273 1
0.2%
-0.48055 1
0.2%
ValueCountFrequency (%)
0.63091 1
0.2%
0.62866 1
0.2%
0.62641 1
0.2%
0.62415 1
0.2%
0.62188 1
0.2%
0.6196 1
0.2%
0.61732 1
0.2%
0.61503 1
0.2%
0.61274 1
0.2%
0.61044 1
0.2%

Interactions

2023-12-12T21:24:22.859577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:24:22.651473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:24:22.976386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:24:22.759229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:24:24.015031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.0000.991
기온반응도(f(s))0.9911.000
2023-12-12T21:24:24.104224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.000-1.000
기온반응도(f(s))-1.0001.000

Missing values

2023-12-12T21:24:23.103889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:24:23.189256image/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

기온(s)기온반응도(f(s))
0-20.00.63091
1-19.90.62866
2-19.80.62641
3-19.70.62415
4-19.60.62188
5-19.50.6196
6-19.40.61732
7-19.30.61503
8-19.20.61274
9-19.10.61044
기온(s)기온반응도(f(s))
59139.1-0.48055
59239.2-0.48273
59339.3-0.48492
59439.4-0.48711
59539.5-0.48932
59639.6-0.49153
59739.7-0.49375
59839.8-0.49597
59939.9-0.4982
60040.0-0.50044