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 수도권 도시가스 수요에 미치는 영향의 상대적 중요도를 측정할 수 있다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15088544/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 10:05:19.585120
Analysis finished2023-12-12 10:05:20.375572
Duration0.79 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
2023-12-12T19:05:20.473401image/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
2023-12-12T19:05:20.682071image/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 

Distinct598
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067159185
Minimum-1.0669
Maximum0.88131
Zeros0
Zeros (%)0.0%
Negative262
Negative (%)43.6%
Memory size5.4 KiB
2023-12-12T19:05:20.852989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.0669
5-th percentile-1.0534
Q1-0.74674
median0.34552
Q30.77721
95-th percentile0.87528
Maximum0.88131
Range1.94821
Interquartile range (IQR)1.52395

Descriptive statistics

Standard deviation0.75486027
Coefficient of variation (CV)11.239866
Kurtosis-1.623721
Mean0.067159185
Median Absolute Deviation (MAD)0.51553
Skewness-0.31926374
Sum40.36267
Variance0.56981402
MonotonicityNot monotonic
2023-12-12T19:05:21.023303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87286 2
 
0.3%
0.7322 2
 
0.3%
0.78587 2
 
0.3%
-0.63597 1
 
0.2%
-0.57661 1
 
0.2%
-0.58524 1
 
0.2%
-0.59381 1
 
0.2%
-0.60234 1
 
0.2%
-0.61082 1
 
0.2%
-0.61926 1
 
0.2%
Other values (588) 588
97.8%
ValueCountFrequency (%)
-1.0669 1
0.2%
-1.06687 1
0.2%
-1.06681 1
0.2%
-1.06672 1
0.2%
-1.06661 1
0.2%
-1.06645 1
0.2%
-1.06629 1
0.2%
-1.06606 1
0.2%
-1.06585 1
0.2%
-1.06556 1
0.2%
ValueCountFrequency (%)
0.88131 1
0.2%
0.8813 1
0.2%
0.88126 1
0.2%
0.88124 1
0.2%
0.88116 1
0.2%
0.88113 1
0.2%
0.88101 1
0.2%
0.88097 1
0.2%
0.8808 1
0.2%
0.88076 1
0.2%

Interactions

2023-12-12T19:05:19.949475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:19.674980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:20.097888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:19.794786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:05:21.136564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.0000.945
기온반응도(f(s))0.9451.000
2023-12-12T19:05:21.227023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기온(s)기온반응도(f(s))
기온(s)1.000-0.855
기온반응도(f(s))-0.8551.000

Missing values

2023-12-12T19:05:20.268284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:05:20.346521image/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))
0-20.00.81671
1-19.90.81289
2-19.80.80917
3-19.70.80555
4-19.60.80203
5-19.50.79861
6-19.40.79528
7-19.30.79205
8-19.20.78891
9-19.10.78587
기온(s)기온반응도(f(s))
59139.1-0.55616
59239.2-0.54517
59339.3-0.53408
59439.4-0.52288
59539.5-0.51158
59639.6-0.50018
59739.7-0.48867
59839.8-0.47706
59939.9-0.46535
60040.0-0.45354