Overview

Dataset statistics

Number of variables2
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory676.0 B
Average record size in memory21.1 B

Variable types

Text1
Numeric1

Dataset

Description특수일은 국경일, 공휴일 등을 의미한다. 일반적으로 공휴일에는 근무일에 비하여 도시가스 수요가 감소하는 패턴을 보이기에, 특수일 효과는 그러한 감소 패턴을 특수일 별로 추정하는 것이다. 고려하는 공휴일은 월요일, 토요일, 일요일, 국경일, 공휴일, 명절, 임시휴일, 샌드위치데이, 연말, 기타 수요에 영향을 미치는 날을 고려한다. 구체적으로 대전 도시가스 수요가 해당 특수일에 근무일 대비 감소하는 비율을 추정한다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15088552/fileData.do

Alerts

특수일 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:18:18.555821
Analysis finished2023-12-11 23:18:18.862916
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

특수일
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T08:18:18.976720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.40625
Min length3

Characters and Unicode

Total characters141
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row근무일
2nd row월요일
3rd row토요일
4th row일요일
5th row1월 1일
ValueCountFrequency (%)
1일 4
 
9.5%
추석 4
 
9.5%
설날 3
 
7.1%
당일 2
 
4.8%
2일 2
 
4.8%
근무일 1
 
2.4%
석가탄신일 1
 
2.4%
하계휴가 1
 
2.4%
샌드위치데이 1
 
2.4%
선거일 1
 
2.4%
Other values (22) 22
52.4%
2023-12-12T08:18:19.267637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 24
17.0%
10
 
7.1%
1 8
 
5.7%
석 7
 
5.0%
날 6
 
4.3%
설 6
 
4.3%
+ 6
 
4.3%
추 6
 
4.3%
2 5
 
3.5%
절 5
 
3.5%
Other values (44) 58
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105
74.5%
Decimal Number 16
 
11.3%
Space Separator 10
 
7.1%
Math Symbol 6
 
4.3%
Dash Punctuation 4
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 24
22.9%
석 7
 
6.7%
날 6
 
5.7%
설 6
 
5.7%
추 6
 
5.7%
절 5
 
4.8%
월 3
 
2.9%
요 3
 
2.9%
스 2
 
1.9%
당 2
 
1.9%
Other values (38) 41
39.0%
Decimal Number
ValueCountFrequency (%)
1 8
50.0%
2 5
31.2%
3 3
 
18.8%
Space Separator
ValueCountFrequency (%)
10
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 105
74.5%
Common 36
 
25.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 24
22.9%
석 7
 
6.7%
날 6
 
5.7%
설 6
 
5.7%
추 6
 
5.7%
절 5
 
4.8%
월 3
 
2.9%
요 3
 
2.9%
스 2
 
1.9%
당 2
 
1.9%
Other values (38) 41
39.0%
Common
ValueCountFrequency (%)
10
27.8%
1 8
22.2%
+ 6
16.7%
2 5
13.9%
- 4
 
11.1%
3 3
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 105
74.5%
ASCII 36
 
25.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 24
22.9%
석 7
 
6.7%
날 6
 
5.7%
설 6
 
5.7%
추 6
 
5.7%
절 5
 
4.8%
월 3
 
2.9%
요 3
 
2.9%
스 2
 
1.9%
당 2
 
1.9%
Other values (38) 41
39.0%
ASCII
ValueCountFrequency (%)
10
27.8%
1 8
22.2%
+ 6
16.7%
2 5
13.9%
- 4
 
11.1%
3 3
 
8.3%

추정치
Real number (ℝ)

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.14421875
Minimum-0.549
Maximum1
Zeros0
Zeros (%)0.0%
Negative31
Negative (%)96.9%
Memory size420.0 B
2023-12-12T08:18:19.370059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.549
5-th percentile-0.4675
Q1-0.19
median-0.1355
Q3-0.09125
95-th percentile-0.0407
Maximum1
Range1.549
Interquartile range (IQR)0.09875

Descriptive statistics

Standard deviation0.24401913
Coefficient of variation (CV)-1.692007
Kurtosis16.195999
Mean-0.14421875
Median Absolute Deviation (MAD)0.05
Skewness3.2002648
Sum-4.615
Variance0.059545338
MonotonicityNot monotonic
2023-12-12T08:18:19.486790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-0.163 2
 
6.2%
-0.185 2
 
6.2%
1.0 1
 
3.1%
-0.256 1
 
3.1%
-0.138 1
 
3.1%
-0.06 1
 
3.1%
-0.047 1
 
3.1%
-0.072 1
 
3.1%
-0.343 1
 
3.1%
-0.484 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
-0.549 1
3.1%
-0.484 1
3.1%
-0.454 1
3.1%
-0.343 1
3.1%
-0.319 1
3.1%
-0.256 1
3.1%
-0.247 1
3.1%
-0.202 1
3.1%
-0.186 1
3.1%
-0.185 2
6.2%
ValueCountFrequency (%)
1.0 1
3.1%
-0.033 1
3.1%
-0.047 1
3.1%
-0.049 1
3.1%
-0.06 1
3.1%
-0.072 1
3.1%
-0.079 1
3.1%
-0.089 1
3.1%
-0.092 1
3.1%
-0.108 1
3.1%

Interactions

2023-12-12T08:18:18.642303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:18:19.559030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수일추정치
특수일1.0001.000
추정치1.0001.000

Missing values

2023-12-12T08:18:18.786769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:18:18.841602image/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

특수일추정치
0근무일1.0
1월요일-0.033
2토요일-0.125
3일요일-0.202
41월 1일-0.173
5삼일절-0.12
6노동절-0.163
7식목일-0.092
8어린이날-0.183
9현충일-0.131
특수일추정치
22추석-2일-0.185
23추석-1일-0.454
24추석 당일-0.549
25추석 +1일-0.484
26추석 +2일-0.343
27추석 +3일-0.185
28선거일-0.072
29샌드위치데이-0.047
30하계휴가-0.06
31임시공휴일-0.138