PyRank

azureml-contrib-datadrift

Azure Machine Learning datadrift
PyPI
More links
Download Stats
Monthly downloads
305
Weekly downloads
105
GitHub stars
-

Downloads in the 30 and 7 days to October 10, 2026. Refreshed weekly.

Package Information

Latest Version
1.0.85
Requires Python
Not specified
Licence
Proprietary https://aka.ms/azureml-preview-sdk-license
Last Release
6 years ago
Author
Microsoft Corp
First Release
April 22, 2019
Total Releases
23
No reported malware or high/critical advisories · Details

The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

Package Insights

What's this?
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
2460 days ago
Monthly downloads
305
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
305
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
15
Month daily avg
10
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
More insights and metrics
Python version support tiers
requires_python
Not specified
Cutting Edge
Minimum supported version is 3.14 or newer
Modern
Minimum supported version is 3.11 to 3.13
Legacy
Supports 3.10 or older (or Python 2)
• Unknown
No requires_python specified or unparseable
Package does not declare a minimum Python version.
License categories
License
Proprietary https://aka.ms/azureml-preview-sdk-license
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
2460 days ago
Total releases
23
Releases/year
3.1
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
305
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

Dependencies

15
7 optional dependencies
  • cryptography==1.5[databricks]
    extra == "databricks"
  • ipython==2.2.0[databricks]
    extra == "databricks"
  • numpy<=1.16.2,>=1.11.0[databricks]
    extra == "databricks"
  • pandas<=0.23.4,>=0.21.0[databricks]
    extra == "databricks"
  • pyopenssl==16.0.0[databricks]
    extra == "databricks"
  • scipy<=1.1.0,>=1.0.0[databricks]
    extra == "databricks"
  • tornado==5.1.1[databricks]
    extra == "databricks"

Used By

0
Not used by any tracked packages

Repository details

Forks
-
Issues
-
Last Commit
-
Contributors
-
Open PRs
-
Archived
-

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Sources and methodology