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 광주 산업용 도시가스 수요에 미치는 영향의 상대적 중요도를 측정할 수 있다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15088569/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 02:14:36.743494
Analysis finished2023-12-12 02:14:37.645373
Duration0.9 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
2023-12-12T11:14:37.725865image/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
2023-12-12T11:14:37.901683image/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%
Mean1.6638935 × 10-8
Minimum-0.30135
Maximum0.43405
Zeros0
Zeros (%)0.0%
Negative346
Negative (%)57.6%
Memory size5.4 KiB
2023-12-12T11:14:38.060173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.30135
5-th percentile-0.2705
Q1-0.19259
median-0.06657
Q30.19237
95-th percentile0.3967
Maximum0.43405
Range0.7354
Interquartile range (IQR)0.38496

Descriptive statistics

Standard deviation0.22263769
Coefficient of variation (CV)13380525
Kurtosis-1.1131168
Mean1.6638935 × 10-8
Median Absolute Deviation (MAD)0.1558
Skewness0.50846708
Sum9.9999999 × 10-6
Variance0.049567541
MonotonicityStrictly decreasing
2023-12-12T11:14:38.244598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43405 1
 
0.2%
-0.15999 1
 
0.2%
-0.16136 1
 
0.2%
-0.16204 1
 
0.2%
-0.16272 1
 
0.2%
-0.16339 1
 
0.2%
-0.16405 1
 
0.2%
-0.16471 1
 
0.2%
-0.16537 1
 
0.2%
-0.16602 1
 
0.2%
Other values (591) 591
98.3%
ValueCountFrequency (%)
-0.30135 1
0.2%
-0.30022 1
0.2%
-0.29909 1
0.2%
-0.29797 1
0.2%
-0.29686 1
0.2%
-0.29576 1
0.2%
-0.29467 1
0.2%
-0.29358 1
0.2%
-0.2925 1
0.2%
-0.29142 1
0.2%
ValueCountFrequency (%)
0.43405 1
0.2%
0.43291 1
0.2%
0.43177 1
0.2%
0.43061 1
0.2%
0.42945 1
0.2%
0.42828 1
0.2%
0.42711 1
0.2%
0.42593 1
0.2%
0.42474 1
0.2%
0.42354 1
0.2%

Interactions

2023-12-12T11:14:37.026813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:14:36.810375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:14:37.130370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:14:36.917844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:14:38.373855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.0000.982
기온반응도(f(s))0.9821.000
2023-12-12T11:14:38.485574image/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

2023-12-12T11:14:37.544124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:14:37.617621image/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))
0-20.00.43405
1-19.90.43291
2-19.80.43177
3-19.70.43061
4-19.60.42945
5-19.50.42828
6-19.40.42711
7-19.30.42593
8-19.20.42474
9-19.10.42354
기온(s)기온반응도(f(s))
59139.1-0.29142
59239.2-0.2925
59339.3-0.29358
59439.4-0.29467
59539.5-0.29576
59639.6-0.29686
59739.7-0.29797
59839.8-0.29909
59939.9-0.30022
60040.0-0.30135