Package Insights
Score based on PyPI data only - no linked source repository
N/A
Health Score
Not available
requires GitHub data
How the maintenance status was determined
Last release
15 days ago
Monthly downloads
431
•
Active
Activity within 30 days
Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
431
Stars
Unknown
Stars per 1k downloads
—
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
•
Unknown
Fewer than 1,000 monthly downloads, or no star data
Needs at least 1,000 monthly downloads to classify popularity.
How download momentum is calculated
Week daily avg
6
Month daily avg
14
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
Python version support tiers
requires_python
>=3.10
Cutting Edge
Minimum supported version is 3.12 or newer
•
Modern
Minimum supported version is 3.9, 3.10, or 3.11
Legacy
Supports 3.8 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
MIT
•
Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
15 days ago
Total releases
1
Releases/year
24.3
•
Rapid
Last release within 365 days and > 12 releases/year
Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
Unknown
Monthly downloads
431
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
Balanced
Enough contributors for the package's popularity
•
Unknown
No contributor data available
Contributor count is missing — this usually indicates the package has no linked GitHub repository.
Engagement
0
Issue Burden
-
per 1k stars
Discussions
-
per 1k stars
Fork Ratio
-
contrib / forks
Stale Release
No
PR Velocity
-
Weekly Downloads
GitHub Stars
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.
Downloads by OS
No OS data available
Python Versions
No Python version data available
Top Countries
1.
US
3
US
2.
KR
2
KR
3.
SG
2
SG
Dependencies
13
- hydroeval >=0.1
- matplotlib >=3.8
- numpy >=1.26
- openpyxl >=3.1
- optuna >=3.5
- pandas >=2.0
- pyarrow >=14.0
- pymannkendall >=1.4
- requests >=2.31
- scikit-learn >=1.4
- scipy >=1.11
- seaborn >=0.13
- statsmodels >=0.14
36 optional dependencies
- arviz[bayes]
- contextily[spatial]
- esda[spatial]
- folium[spatial]
- geopandas[spatial]
- h5netcdf[netcdf]
- ipykernel[dev]
- jupyterlab[dev]
- lightgbm[ml]
- lightning[deep]
- missingno[profile]
- mkdocs[docs]
- mkdocs-material[docs]
- mkdocstrings[docs]
- netcdf4[netcdf]
- pip-audit[dev]
- plotly[profile]
- polars[fast]
- pre-commit[dev]
- prophet[prophet]
- pykrige[spatial]
- pymc[bayes]
- pymdown-extensions[docs]
- pyproj[spatial]
- pysal[spatial]
- pytest[dev]
- pytest-cov[dev]
- rasterio[spatial]
- rioxarray[spatial]
- ruff[dev]
- shapely[spatial]
- sweetviz[profile]
- torch[deep]
- xarray[spatial]
- xgboost[ml]
- ydata-profiling[profile]
Used By
0
Not used by any tracked packages
Package Information
License
MIT
First Release
May 7, 2026
Last Release
2 weeks ago
Last Commit
-
Latest Version
1.3.2
Total Releases
1
Contributors
-
Open PRs
-
Archived
-
Author
Dan Méndez
Wheels
py3
Tracking Since
May 7, 2026
Data Updated
May 17, 2026
Data Freshness
Fresh