Dataset statistics
| Number of variables | 2 |
|---|---|
| Number of observations | 339 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| 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 수도권 도시가스 수요의 계절성을 나타낸다. |
|---|---|
| Author | 한국가스공사 |
| URL | https://www.data.go.kr/data/15088520/fileData.do |
연월일 has unique values | Unique |
Reproduction
| Analysis started | 2023-12-12 02:37:08.331271 |
|---|---|
| Analysis finished | 2023-12-12 02:37:08.640211 |
| Duration | 0.31 seconds |
| Software version | ydata-profiling vv4.5.1 |
| Download configuration | config.json |
연월일
Date
UNIQUE
| Distinct | 339 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 2.8 KiB |
| Minimum | 1993-01-31 00:00:00 |
|---|---|
| Maximum | 2021-03-31 00:00:00 |
Histogram with fixed size bins (bins=50)
기온효과
Real number (ℝ)
| Distinct | 338 |
|---|---|
| Distinct (%) | 99.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 0.0011190265 |
| Minimum | -0.94876 |
|---|---|
| Maximum | 0.91023 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 180 |
| Negative (%) | 53.1% |
| Memory size | 3.1 KiB |
Quantile statistics
| Minimum | -0.94876 |
|---|---|
| 5-th percentile | -0.78584 |
| Q1 | -0.53762 |
| median | -0.07265 |
| Q3 | 0.6017 |
| 95-th percentile | 0.805151 |
| Maximum | 0.91023 |
| Range | 1.85899 |
| Interquartile range (IQR) | 1.13932 |
Descriptive statistics
| Standard deviation | 0.56645337 |
|---|---|
| Coefficient of variation (CV) | 506.2019 |
| Kurtosis | -1.5179917 |
| Mean | 0.0011190265 |
| Median Absolute Deviation (MAD) | 0.5187 |
| Skewness | 0.093386185 |
| Sum | 0.37935 |
| Variance | 0.32086942 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| -0.59108 | 2 | 0.6% |
| 0.83115 | 1 | 0.3% |
| -0.5171 | 1 | 0.3% |
| -0.43988 | 1 | 0.3% |
| 0.02237 | 1 | 0.3% |
| 0.47314 | 1 | 0.3% |
| 0.76015 | 1 | 0.3% |
| 0.80509 | 1 | 0.3% |
| 0.74315 | 1 | 0.3% |
| 0.10241 | 1 | 0.3% |
| Other values (328) | 328 |
| Value | Count | Frequency (%) |
| -0.94876 | 1 | |
| -0.94024 | 1 | |
| -0.90075 | 1 | |
| -0.88184 | 1 | |
| -0.85637 | 1 | |
| -0.83921 | 1 | |
| -0.8357 | 1 | |
| -0.83014 | 1 | |
| -0.82731 | 1 | |
| -0.82605 | 1 |
| Value | Count | Frequency (%) |
| 0.91023 | 1 | |
| 0.8416 | 1 | |
| 0.83929 | 1 | |
| 0.83507 | 1 | |
| 0.83115 | 1 | |
| 0.82908 | 1 | |
| 0.82728 | 1 | |
| 0.82423 | 1 | |
| 0.82204 | 1 | |
| 0.81655 | 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.
| 연월일 | 기온효과 | |
|---|---|---|
| 0 | 1993-01-31 | 0.83115 |
| 1 | 1993-02-28 | 0.7673 |
| 2 | 1993-03-31 | 0.58067 |
| 3 | 1993-04-30 | 0.24677 |
| 4 | 1993-05-31 | -0.37278 |
| 5 | 1993-06-30 | -0.65251 |
| 6 | 1993-07-31 | -0.78638 |
| 7 | 1993-08-31 | -0.78578 |
| 8 | 1993-09-30 | -0.62816 |
| 9 | 1993-10-31 | 0.00809 |
| 연월일 | 기온효과 | |
|---|---|---|
| 329 | 2020-06-30 | -0.48971 |
| 330 | 2020-07-31 | -0.51924 |
| 331 | 2020-08-31 | -0.5194 |
| 332 | 2020-09-30 | -0.47611 |
| 333 | 2020-10-31 | -0.15375 |
| 334 | 2020-11-30 | 0.2216 |
| 335 | 2020-12-31 | 0.67759 |
| 336 | 2021-01-31 | 0.74138 |
| 337 | 2021-02-28 | 0.51077 |
| 338 | 2021-03-31 | 0.17655 |