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 대구 산업용 도시가스 수요에 미치는 영향의 상대적 중요도를 측정할 수 있다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15088566/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 14:26:48.073835
Analysis finished2023-12-12 14:26:48.783292
Duration0.71 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
2023-12-12T23:26:48.856487image/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
2023-12-12T23:26:49.288449image/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%
Mean-3.327787 × 10-7
Minimum-0.3317
Maximum0.37812
Zeros0
Zeros (%)0.0%
Negative330
Negative (%)54.9%
Memory size5.4 KiB
2023-12-12T23:26:49.448070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3317
5-th percentile-0.28424
Q1-0.13508
median-0.02329
Q30.135
95-th percentile0.32838
Maximum0.37812
Range0.70982
Interquartile range (IQR)0.27008

Descriptive statistics

Standard deviation0.18380548
Coefficient of variation (CV)-552335.46
Kurtosis-0.80899097
Mean-3.327787 × 10-7
Median Absolute Deviation (MAD)0.13154
Skewness0.26237468
Sum-0.0002
Variance0.033784453
MonotonicityStrictly decreasing
2023-12-12T23:26:49.580583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.37812 1
 
0.2%
-0.08951 1
 
0.2%
-0.09099 1
 
0.2%
-0.09174 1
 
0.2%
-0.09248 1
 
0.2%
-0.09323 1
 
0.2%
-0.09398 1
 
0.2%
-0.09474 1
 
0.2%
-0.09549 1
 
0.2%
-0.09625 1
 
0.2%
Other values (591) 591
98.3%
ValueCountFrequency (%)
-0.3317 1
0.2%
-0.33008 1
0.2%
-0.32845 1
0.2%
-0.32683 1
0.2%
-0.32521 1
0.2%
-0.3236 1
0.2%
-0.32199 1
0.2%
-0.32038 1
0.2%
-0.31877 1
0.2%
-0.31717 1
0.2%
ValueCountFrequency (%)
0.37812 1
0.2%
0.37649 1
0.2%
0.37486 1
0.2%
0.37323 1
0.2%
0.37159 1
0.2%
0.36995 1
0.2%
0.36831 1
0.2%
0.36667 1
0.2%
0.36503 1
0.2%
0.36338 1
0.2%

Interactions

2023-12-12T23:26:48.397369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:26:48.145075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:26:48.492179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:26:48.269847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:26:49.684652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.0000.987
기온반응도(f(s))0.9871.000
2023-12-12T23:26:49.770547image/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

2023-12-12T23:26:48.650486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:26:48.752068image/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))
0-20.00.37812
1-19.90.37649
2-19.80.37486
3-19.70.37323
4-19.60.37159
5-19.50.36995
6-19.40.36831
7-19.30.36667
8-19.20.36503
9-19.10.36338
기온(s)기온반응도(f(s))
59139.1-0.31717
59239.2-0.31877
59339.3-0.32038
59439.4-0.32199
59539.5-0.3236
59639.6-0.32521
59739.7-0.32683
59839.8-0.32845
59939.9-0.33008
60040.0-0.3317