active 2025-01-09 → 2025-01-10 (UTC)
Complete coverage26,650 / 26,650 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-08-29 (UTC)
Events
52
Pushes
1
Pull requests
1
Issues
34
Stars
0
Forks
1
Activity over time
Daily event counts in the loaded window
Line chart, 2 days from 2025-01-09 to 2025-01-10. Pushes: 1 total, peak 1 in a day. Pull requests: 1 total, peak 1 in a day. Issues: 34 total, peak 26 in a day. Comments: 12 total, peak 9 in a day. Stars: 0 total, peak 0 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| Hysnap | 48 | 1 | 1 | 12 |
Recent activity
Latest issues, pull requests and releases
- Pull request#22Hysnap2025-01-10 16:45
- Issue comment#14Hysnap2025-01-10 16:40Update Readme with resources used for reference
- Issue#14Hysnap2025-01-10 16:40Update Readme with resources used for reference
- Issue comment#13Hysnap2025-01-10 16:40Update Readme with issues
- Issue#13Hysnap2025-01-10 16:40Update Readme with issues
- Issue#8Hysnap2025-01-10 15:52Produce calculated difference between holiday and non holiday weeks .. also week on week sale changes.
- Issue#18Hysnap2025-01-10 15:52Produce calculated Year on Year sales change by Department and by Store
- Issue#6Hysnap2025-01-10 15:51Transform: Clean the data, handle missing values, and create new features such as sales differences between holiday and non-holiday weeks.
- Issue#20Hysnap2025-01-10 11:25Replace nulls in markdown columns with 0, use fill forward to replace nulls in CPI and Unemployment, create a total MarkDownvalue and a flag to identify stores+weeks with markdownvalue.
- Issue#15Hysnap2025-01-10 11:25Understand the data structure as an ETL Specialist. Create initial visualisations using Matplotlib and Seaborn. Develop basic charts and graphs to visualise key metrics.
- Issue#12Hysnap2025-01-10 11:24Compare sales performance across different stores and regions
- Issue comment#12Hysnap2025-01-10 11:24Compare sales performance across different stores and regions
- Issue#21Hysnap2025-01-09 19:01Calculate impact of Inflation based off CPI variations., generate a base level of sales excluding inflation and promotional impact.
- Issue#20Hysnap2025-01-09 18:59Replace nulls in markdown columns with 0, use fill forward to replace nulls in CPI and Unemployment, create a total MarkDownvalue and a flag to identify stores+weeks with markdownvalue.
- Issue comment#19Hysnap2025-01-09 16:33Calculate Delta fields for Weekly Sales both Year on Year and Week on Week, aslo create moving 13 week average.
- Issue#19Hysnap2025-01-09 16:33Calculate Delta fields for Weekly Sales both Year on Year and Week on Week, aslo create moving 13 week average.
- Issue#19Hysnap2025-01-09 16:01Calculate Delta fields for Weekly Sales both Year on Year and Week on Week, aslo create moving 13 week average.
- Issue comment#17Hysnap2025-01-09 15:58Mrege Features_df into enhancedsales_df to enable enhanced analysis.
- Issue#17Hysnap2025-01-09 15:58Mrege Features_df into enhancedsales_df to enable enhanced analysis.
- Issue#18Hysnap2025-01-09 15:57Produce calculated Year on Year sales change by Department and by Store
- Issue#17Hysnap2025-01-09 15:33Mrege Features_df into enhancedsales_df to enable enhanced analysis.
- Issue#16Hysnap2025-01-09 15:32Enhance weekly sales data with store attributes
- Issue comment#16Hysnap2025-01-09 15:32Enhance weekly sales data with store attributes
- Issue comment#15Hysnap2025-01-09 13:36Understand the data structure as an ETL Specialist. Create initial visualisations using Matplotlib and Seaborn. Develop basic charts and graphs to visualise key metrics.
- Issue comment#6Hysnap2025-01-09 13:36Transform: Clean the data, handle missing values, and create new features such as sales differences between holiday and non-holiday weeks.
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