Overview

Dataset statistics

Number of variables2
Number of observations339
Missing cells350
Missing cells (%)51.6%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory5.8 KiB
Average record size in memory17.4 B

Variable types

DateTime1
Numeric1

Dataset

Description기온효과 산출 과정은 기온반응함수와 기온효과의 곱을 기온에 대하여 적분하여 생성한다. 이러한 산출과정의 의미는 기온반응함수는 각 온도가 발생시키는 수도권 도시가스수요를 축정하여, 기온 분포함수는 각 온도의 발생횟수를 측정함으로써 최종적으로 해당 기온에서 발생하는 도시가스 수요를 측정한다. 따라서 기온효과는 월 단위 대구 도시가스 수요의 계절성을 나타낸다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15088531/fileData.do

Alerts

Dataset has 1 (0.3%) duplicate rowsDuplicates
연월일 has 175 (51.6%) missing valuesMissing
기온효과 has 175 (51.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 11:03:06.342823
Analysis finished2023-12-12 11:03:06.846801
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월일
Date

MISSING 

Distinct164
Distinct (%)100.0%
Missing175
Missing (%)51.6%
Memory size2.8 KiB
Minimum2005-01-31 00:00:00
Maximum2018-08-31 00:00:00
2023-12-12T20:03:06.937939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:03:07.133857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

기온효과
Real number (ℝ)

MISSING 

Distinct163
Distinct (%)99.4%
Missing175
Missing (%)51.6%
Infinite0
Infinite (%)0.0%
Mean6.1739187
Minimum6.04695
Maximum6.33161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-12T20:03:07.310657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.04695
5-th percentile6.061106
Q16.106595
median6.164
Q36.2341075
95-th percentile6.29386
Maximum6.33161
Range0.28466
Interquartile range (IQR)0.1275125

Descriptive statistics

Standard deviation0.077322363
Coefficient of variation (CV)0.012524033
Kurtosis-1.2047911
Mean6.1739187
Median Absolute Deviation (MAD)0.060455
Skewness0.18610112
Sum1012.5227
Variance0.0059787478
MonotonicityNot monotonic
2023-12-12T20:03:07.503982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.27594 2
 
0.6%
6.10882 1
 
0.3%
6.21617 1
 
0.3%
6.26405 1
 
0.3%
6.27155 1
 
0.3%
6.25611 1
 
0.3%
6.21072 1
 
0.3%
6.16602 1
 
0.3%
6.12041 1
 
0.3%
6.10434 1
 
0.3%
Other values (153) 153
45.1%
(Missing) 175
51.6%
ValueCountFrequency (%)
6.04695 1
0.3%
6.05172 1
0.3%
6.05204 1
0.3%
6.05224 1
0.3%
6.05622 1
0.3%
6.05756 1
0.3%
6.06004 1
0.3%
6.06011 1
0.3%
6.06092 1
0.3%
6.06216 1
0.3%
ValueCountFrequency (%)
6.33161 1
0.3%
6.31615 1
0.3%
6.30561 1
0.3%
6.30542 1
0.3%
6.30521 1
0.3%
6.30486 1
0.3%
6.30304 1
0.3%
6.29443 1
0.3%
6.2941 1
0.3%
6.2925 1
0.3%

Interactions

2023-12-12T20:03:06.393781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T20:03:06.555275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:03:06.660008image/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.
2023-12-12T20:03:06.776456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연월일기온효과
02005-01-316.2925
12005-02-286.28738
22005-03-316.23235
32005-04-306.16228
42005-05-316.13615
52005-06-306.09011
62005-07-316.07593
72005-08-316.07699
82005-09-306.11036
92005-10-316.16341
연월일기온효과
329<NA><NA>
330<NA><NA>
331<NA><NA>
332<NA><NA>
333<NA><NA>
334<NA><NA>
335<NA><NA>
336<NA><NA>
337<NA><NA>
338<NA><NA>

Duplicate rows

Most frequently occurring

연월일기온효과# duplicates
0<NA><NA>175