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 대전 도시가스 수요의 계절성을 나타낸다. |
|---|---|
| Author | 한국가스공사 |
| URL | https://www.data.go.kr/data/15088528/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 15:30:32.412479 |
|---|---|
| Analysis finished | 2023-12-12 15:30:32.862387 |
| Duration | 0.45 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 | 3.9560653 |
| Minimum | 3.75929 |
|---|---|
| Maximum | 4.26137 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 3.1 KiB |
Quantile statistics
| Minimum | 3.75929 |
|---|---|
| 5-th percentile | 3.7788315 |
| Q1 | 3.8240475 |
| median | 3.9235 |
| Q3 | 4.0803575 |
| 95-th percentile | 4.1844515 |
| Maximum | 4.26137 |
| Range | 0.50208 |
| Interquartile range (IQR) | 0.25631 |
Descriptive statistics
| Standard deviation | 0.14341612 |
|---|---|
| Coefficient of variation (CV) | 0.036252212 |
| Kurtosis | -1.2870631 |
| Mean | 3.9560653 |
| Median Absolute Deviation (MAD) | 0.11498 |
| Skewness | 0.3574624 |
| Sum | 648.79471 |
| Variance | 0.020568183 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 3.83179 | 1 | 0.3% |
| 4.04174 | 1 | 0.3% |
| 4.13611 | 1 | 0.3% |
| 4.15847 | 1 | 0.3% |
| 4.10982 | 1 | 0.3% |
| 4.01642 | 1 | 0.3% |
| 3.92406 | 1 | 0.3% |
| 3.85813 | 1 | 0.3% |
| 3.81549 | 1 | 0.3% |
| 3.78643 | 1 | 0.3% |
| Other values (154) | 154 | |
| (Missing) | 175 |
| Value | Count | Frequency (%) |
| 3.75929 | 1 | |
| 3.76875 | 1 | |
| 3.76914 | 1 | |
| 3.76915 | 1 | |
| 3.77495 | 1 | |
| 3.77528 | 1 | |
| 3.77605 | 1 | |
| 3.77789 | 1 | |
| 3.77859 | 1 | |
| 3.7802 | 1 |
| Value | Count | Frequency (%) |
| 4.26137 | 1 | |
| 4.21141 | 1 | |
| 4.21067 | 1 | |
| 4.20757 | 1 | |
| 4.20652 | 1 | |
| 4.20267 | 1 | |
| 4.18975 | 1 | |
| 4.18716 | 1 | |
| 4.18487 | 1 | |
| 4.18208 | 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 | 4.18487 |
| 1 | 2005-02-28 | 4.17665 |
| 2 | 2005-03-31 | 4.07548 |
| 3 | 2005-04-30 | 3.93435 |
| 4 | 2005-05-31 | 3.881 |
| 5 | 2005-06-30 | 3.81882 |
| 6 | 2005-07-31 | 3.79072 |
| 7 | 2005-08-31 | 3.79395 |
| 8 | 2005-09-30 | 3.82462 |
| 9 | 2005-10-31 | 3.92333 |
| 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 |