Dataset statistics
| Number of variables | 2 |
|---|---|
| Number of observations | 339 |
| Missing cells | 350 |
| Missing cells (%) | 51.6% |
| Duplicate rows | 1 |
| Duplicate rows (%) | 0.3% |
| Total size in memory | 5.8 KiB |
| Average record size in memory | 17.4 B |
Variable types
| DateTime | 1 |
|---|---|
| Numeric | 1 |
Dataset
| Description | 기온효과 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 부산 도시가스 수요의 계절성을 나타낸다. |
|---|---|
| Author | 한국가스공사 |
| URL | https://www.data.go.kr/data/15088538/fileData.do |
| Dataset has 1 (0.3%) duplicate rows | Duplicates |
연월일 has 175 (51.6%) missing values | Missing |
기온효과 has 175 (51.6%) missing values | Missing |
Reproduction
| Analysis started | 2023-12-12 06:47:24.136439 |
|---|---|
| Analysis finished | 2023-12-12 06:47:24.559811 |
| Duration | 0.42 seconds |
| Software version | ydata-profiling vv4.5.1 |
| Download configuration | config.json |
연월일
Date
MISSING
| Distinct | 164 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 175 |
| Missing (%) | 51.6% |
| Memory size | 2.8 KiB |
| Minimum | 2005-01-31 00:00:00 |
|---|---|
| Maximum | 2018-08-31 00:00:00 |
Histogram with fixed size bins (bins=50)
기온효과
Real number (ℝ)
MISSING
| Distinct | 164 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 175 |
| Missing (%) | 51.6% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 5.6369282 |
| Minimum | 5.46602 |
|---|---|
| Maximum | 5.90313 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 3.1 KiB |
Quantile statistics
| Minimum | 5.46602 |
|---|---|
| 5-th percentile | 5.483716 |
| Q1 | 5.54057 |
| median | 5.613885 |
| Q3 | 5.73053 |
| 95-th percentile | 5.827739 |
| Maximum | 5.90313 |
| Range | 0.43711 |
| Interquartile range (IQR) | 0.18996 |
Descriptive statistics
| Standard deviation | 0.1130284 |
|---|---|
| Coefficient of variation (CV) | 0.020051417 |
| Kurtosis | -1.0845079 |
| Mean | 5.6369282 |
| Median Absolute Deviation (MAD) | 0.084895 |
| Skewness | 0.35466465 |
| Sum | 924.45622 |
| Variance | 0.012775419 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 5.52016 | 1 | 0.3% |
| 5.68612 | 1 | 0.3% |
| 5.77506 | 1 | 0.3% |
| 5.78573 | 1 | 0.3% |
| 5.76711 | 1 | 0.3% |
| 5.69975 | 1 | 0.3% |
| 5.62934 | 1 | 0.3% |
| 5.57845 | 1 | 0.3% |
| 5.5471 | 1 | 0.3% |
| 5.50733 | 1 | 0.3% |
| Other values (154) | 154 | |
| (Missing) | 175 |
| Value | Count | Frequency (%) |
| 5.46602 | 1 | |
| 5.46739 | 1 | |
| 5.46792 | 1 | |
| 5.46854 | 1 | |
| 5.47202 | 1 | |
| 5.47274 | 1 | |
| 5.47896 | 1 | |
| 5.48054 | 1 | |
| 5.48329 | 1 | |
| 5.48613 | 1 |
| Value | Count | Frequency (%) |
| 5.90313 | 1 | |
| 5.86093 | 1 | |
| 5.84851 | 1 | |
| 5.84242 | 1 | |
| 5.83439 | 1 | |
| 5.83331 | 1 | |
| 5.82845 | 1 | |
| 5.82843 | 1 | |
| 5.82809 | 1 | |
| 5.82575 | 1 |
A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
| 연월일 | 기온효과 | |
|---|---|---|
| 0 | 2005-01-31 | 5.84242 |
| 1 | 2005-02-28 | 5.83331 |
| 2 | 2005-03-31 | 5.74575 |
| 3 | 2005-04-30 | 5.63755 |
| 4 | 2005-05-31 | 5.59753 |
| 5 | 2005-06-30 | 5.55137 |
| 6 | 2005-07-31 | 5.51796 |
| 7 | 2005-08-31 | 5.49532 |
| 8 | 2005-09-30 | 5.5311 |
| 9 | 2005-10-31 | 5.5976 |
| 연월일 | 기온효과 | |
|---|---|---|
| 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> |
Most frequently occurring
| 연월일 | 기온효과 | # duplicates | |
|---|---|---|---|
| 0 | <NA> | <NA> | 175 |