Dataset statistics
| Number of variables | 2 |
|---|---|
| Number of observations | 54 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 1.0 KiB |
| Average record size in memory | 19.4 B |
Variable types
| DateTime | 1 |
|---|---|
| Numeric | 1 |
Dataset
| Description | 대구광역시 상수도사업본부에서 2019년 1월부터 2023년 6월까지 월별로 검침값이 등록된 전수를 포함하고 있습니다. |
|---|---|
| URL | https://www.data.go.kr/data/15116802/fileData.do |
Reproduction
| Analysis started | 2023-12-11 23:50:32.286760 |
|---|---|
| Analysis finished | 2023-12-11 23:50:32.487548 |
| Duration | 0.2 seconds |
| Software version | ydata-profiling vv4.5.1 |
| Download configuration | config.json |
납기년월
Date
UNIQUE
| Distinct | 54 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 564.0 B |
| Minimum | 2019-01-01 00:00:00 |
|---|---|
| Maximum | 2023-06-01 00:00:00 |
Histogram with fixed size bins (bins=50)
검침전수
Real number (ℝ)
UNIQUE
| Distinct | 54 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 298710.44 |
| Minimum | 294792 |
|---|---|
| Maximum | 306893 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 618.0 B |
Quantile statistics
| Minimum | 294792 |
|---|---|
| 5-th percentile | 294901.75 |
| Q1 | 295431.75 |
| median | 297394.5 |
| Q3 | 301251 |
| 95-th percentile | 306005.1 |
| Maximum | 306893 |
| Range | 12101 |
| Interquartile range (IQR) | 5819.25 |
Descriptive statistics
| Standard deviation | 3706.522 |
|---|---|
| Coefficient of variation (CV) | 0.012408411 |
| Kurtosis | -0.55685559 |
| Mean | 298710.44 |
| Median Absolute Deviation (MAD) | 2298 |
| Skewness | 0.79992588 |
| Sum | 16130364 |
| Variance | 13738305 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 306893 | 1 | 1.9% |
| 294849 | 1 | 1.9% |
| 296874 | 1 | 1.9% |
| 296666 | 1 | 1.9% |
| 296542 | 1 | 1.9% |
| 296377 | 1 | 1.9% |
| 296291 | 1 | 1.9% |
| 296001 | 1 | 1.9% |
| 295824 | 1 | 1.9% |
| 295684 | 1 | 1.9% |
| Other values (44) | 44 |
| Value | Count | Frequency (%) |
| 294792 | 1 | |
| 294849 | 1 | |
| 294853 | 1 | |
| 294928 | 1 | |
| 294973 | 1 | |
| 294990 | 1 | |
| 295002 | 1 | |
| 295080 | 1 | |
| 295113 | 1 | |
| 295197 | 1 |
| Value | Count | Frequency (%) |
| 306893 | 1 | |
| 306574 | 1 | |
| 306308 | 1 | |
| 305842 | 1 | |
| 305380 | 1 | |
| 304982 | 1 | |
| 304448 | 1 | |
| 303633 | 1 | |
| 303050 | 1 | |
| 302494 | 1 |
| 납기년월 | 검침전수 | |
|---|---|---|
| 납기년월 | 1.000 | 1.000 |
| 검침전수 | 1.000 | 1.000 |
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.
| 납기년월 | 검침전수 | |
|---|---|---|
| 0 | 2019-01 | 306893 |
| 1 | 2019-02 | 306574 |
| 2 | 2019-03 | 306308 |
| 3 | 2019-04 | 305842 |
| 4 | 2019-05 | 305380 |
| 5 | 2019-06 | 304982 |
| 6 | 2019-07 | 304448 |
| 7 | 2019-08 | 303633 |
| 8 | 2019-09 | 303050 |
| 9 | 2019-10 | 302494 |
| 납기년월 | 검침전수 | |
|---|---|---|
| 44 | 2022-09 | 294928 |
| 45 | 2022-10 | 294973 |
| 46 | 2022-11 | 295002 |
| 47 | 2022-12 | 295080 |
| 48 | 2023-01 | 295113 |
| 49 | 2023-02 | 295197 |
| 50 | 2023-03 | 295214 |
| 51 | 2023-04 | 295355 |
| 52 | 2023-05 | 295485 |
| 53 | 2023-06 | 295631 |