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 서울 산업용 도시가스 수요에 미치는 영향의 상대적 중요도를 측정할 수 있다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15088563/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

Reproduction

Analysis started2023-12-12 12:25:03.756996
Analysis finished2023-12-12 12:25:04.632577
Duration0.88 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
2023-12-12T21:25:04.722810image/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
2023-12-12T21:25:04.923085image/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 

Distinct599
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.9833611 × 10-8
Minimum-0.56945
Maximum0.28734
Zeros0
Zeros (%)0.0%
Negative305
Negative (%)50.7%
Memory size5.4 KiB
2023-12-12T21:25:05.116537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.56945
5-th percentile-0.41177
Q1-0.12517
median-0.00592
Q30.1972
95-th percentile0.28331
Maximum0.28734
Range0.85679
Interquartile range (IQR)0.32237

Descriptive statistics

Standard deviation0.21439719
Coefficient of variation (CV)-2147545.2
Kurtosis-0.44345281
Mean-9.9833611 × 10-8
Median Absolute Deviation (MAD)0.16098
Skewness-0.49129687
Sum-6 × 10-5
Variance0.045966157
MonotonicityNot monotonic
2023-12-12T21:25:05.291053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0578 2
 
0.3%
0.28545 2
 
0.3%
-0.10012 1
 
0.2%
-0.09957 1
 
0.2%
-0.09903 1
 
0.2%
-0.09849 1
 
0.2%
-0.09796 1
 
0.2%
-0.09742 1
 
0.2%
-0.09689 1
 
0.2%
-0.09209 1
 
0.2%
Other values (589) 589
98.0%
ValueCountFrequency (%)
-0.56945 1
0.2%
-0.56352 1
0.2%
-0.55763 1
0.2%
-0.55179 1
0.2%
-0.546 1
0.2%
-0.54026 1
0.2%
-0.53456 1
0.2%
-0.52891 1
0.2%
-0.52331 1
0.2%
-0.51775 1
0.2%
ValueCountFrequency (%)
0.28734 1
0.2%
0.28733 1
0.2%
0.28731 1
0.2%
0.28729 1
0.2%
0.28724 1
0.2%
0.28722 1
0.2%
0.28714 1
0.2%
0.28712 1
0.2%
0.28701 1
0.2%
0.28698 1
0.2%

Interactions

2023-12-12T21:25:04.192693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:25:03.875328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:25:04.346373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:25:04.029467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:25:05.417466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.0000.963
기온반응도(f(s))0.9631.000
2023-12-12T21:25:05.546865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.000-0.940
기온반응도(f(s))-0.9401.000

Missing values

2023-12-12T21:25:04.497430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:25:04.589008image/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))
0-20.00.05319
1-19.90.0578
2-19.80.06237
3-19.70.06689
4-19.60.07137
5-19.50.07579
6-19.40.08017
7-19.30.0845
8-19.20.08878
9-19.10.09302
기온(s)기온반응도(f(s))
59139.1-0.51775
59239.2-0.52331
59339.3-0.52891
59439.4-0.53456
59539.5-0.54026
59639.6-0.546
59739.7-0.55179
59839.8-0.55763
59939.9-0.56352
60040.0-0.56945