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 대전 산업용 도시가스 수요에 미치는 영향의 상대적 중요도를 측정할 수 있다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15088564/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 16:57:45.560319
Analysis finished2023-12-12 16:57:46.208384
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
2023-12-13T01:57:46.289266image/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
2023-12-13T01:57:46.436391image/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-1.9966722 × 10-7
Minimum-0.3654
Maximum0.44676
Zeros0
Zeros (%)0.0%
Negative325
Negative (%)54.1%
Memory size5.4 KiB
2023-12-13T01:57:46.596150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3654
5-th percentile-0.33473
Q1-0.22882
median-0.04082
Q30.22869
95-th percentile0.41211
Maximum0.44676
Range0.81216
Interquartile range (IQR)0.45751

Descriptive statistics

Standard deviation0.25110469
Coefficient of variation (CV)-1257616
Kurtosis-1.2918268
Mean-1.9966722 × 10-7
Median Absolute Deviation (MAD)0.21505
Skewness0.27306677
Sum-0.00012
Variance0.063053567
MonotonicityStrictly decreasing
2023-12-13T01:57:46.743671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44676 1
 
0.2%
-0.17242 1
 
0.2%
-0.17469 1
 
0.2%
-0.17582 1
 
0.2%
-0.17695 1
 
0.2%
-0.17807 1
 
0.2%
-0.17919 1
 
0.2%
-0.1803 1
 
0.2%
-0.18141 1
 
0.2%
-0.18251 1
 
0.2%
Other values (591) 591
98.3%
ValueCountFrequency (%)
-0.3654 1
0.2%
-0.36432 1
0.2%
-0.36324 1
0.2%
-0.36217 1
0.2%
-0.3611 1
0.2%
-0.36004 1
0.2%
-0.35898 1
0.2%
-0.35792 1
0.2%
-0.35687 1
0.2%
-0.35582 1
0.2%
ValueCountFrequency (%)
0.44676 1
0.2%
0.44568 1
0.2%
0.44459 1
0.2%
0.44349 1
0.2%
0.44239 1
0.2%
0.44129 1
0.2%
0.44018 1
0.2%
0.43906 1
0.2%
0.43794 1
0.2%
0.43682 1
0.2%

Interactions

2023-12-13T01:57:45.823413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:57:45.620302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:57:45.940799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:57:45.705180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:57:47.128728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.0000.993
기온반응도(f(s))0.9931.000
2023-12-13T01:57:47.206208image/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

2023-12-13T01:57:46.126568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:57:46.184770image/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))
0-20.00.44676
1-19.90.44568
2-19.80.44459
3-19.70.44349
4-19.60.44239
5-19.50.44129
6-19.40.44018
7-19.30.43906
8-19.20.43794
9-19.10.43682
기온(s)기온반응도(f(s))
59139.1-0.35582
59239.2-0.35687
59339.3-0.35792
59439.4-0.35898
59539.5-0.36004
59639.6-0.3611
59739.7-0.36217
59839.8-0.36324
59939.9-0.36432
60040.0-0.3654