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This is a package that allows extraction of data from pandas dataframes (time-series like, but this is not a strict requirement) with a dedicated integrated alerting module based on envvars.

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  • [x ] Title of this PR is meaningful: e.g. "Adding my_nifty_package", not "updated meta.yaml".
  • [x ] License file is packaged (see here for an example).
  • [ x] Source is from official source.
  • [ x] Package does not vendor other packages. (If a package uses the source of another package, they should be separate packages or the licenses of all packages need to be packaged).
  • If static libraries are linked in, the license of the static library is packaged.
  • [ x] Package does not ship static libraries. If static libraries are needed, follow CFEP-18.
  • [ x] Build number is 0.
  • [ x] A tarball (url) rather than a repo (e.g. git_url) is used in your recipe (see here for more details).
  • [ x] GitHub users listed in the maintainer section have posted a comment confirming they are willing to be listed there.
  • [x ] When in trouble, please check our knowledge base documentation before pinging a team.

A small, dependency-light toolkit built around two complementary components:

DataFrame time-window extraction (PBM_SUPPORT_DF_WINDOW)

Extract an ordered, fixed-length hour window from a pandas DataFrame.
Robust coercion (auto / float / raw) and a diagnostic engine.
Optional “alert bridge” that can publish a visible monitoring signal when diagnostics fail.
Environment-variable monitoring integration layer (alerting_subsystem)

A transport-agnostic monitoring convention based on environment variables.
Cache + aggregation helpers and a human-readable “post” renderer (text or HTML).
A simple external-exception backstop channel.
The modules can be used independently, but they are designed to work well together:
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Hi! This is the staged-recipes linter and your PR looks excellent! 🚀

@conda-forge-admin
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Hi! This is the friendly automated conda-forge-linting service.

I wanted to let you know that I linted all conda-recipes in your PR (recipes/dataframe-timeseries-mon/meta.yaml) and found some lint.

Here's what I've got...

For recipes/dataframe-timeseries-mon/meta.yaml:

  • requirements: run: python >= 9999 should not contain a space between relational operator and the version, i.e. python >=9999

For recipes/dataframe-timeseries-mon/meta.yaml:

  • ℹ️ noarch: python recipes should usually follow the syntax in our documentation for specifying the Python version.
    • For the run section of the recipe, you should usually use the pin python >={{ python_min }} for the python entry.
    • If the package requires a newer Python version than the currently supported minimum version on conda-forge, you can override the python_min variable by adding a Jinja2 set statement at the top of your recipe (or using an equivalent context variable for v1 recipes).
  • ℹ️ top-level output has some malformed specs:
  • In section run: python >= 9999
    Requirement spec fields should match the syntax name [version [build]]to avoid known issues in conda-build. For example, instead of name =version=build, use name version.* build. There should be no spaces between version operators and versions either: python >= 3.8 should be python >=3.8.

This message was generated by GitHub Actions workflow run https://github.com/conda-forge/conda-forge-webservices/actions/runs/21119583952. Examine the logs at this URL for more detail.

@Marcin-kowalczyk-polenergia
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I confirm I am willing to be a maintainer of this feedstock and my GitHub handle matches the one in recipe-maintainers.

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Hi! This is the friendly automated conda-forge-linting service.

I just wanted to let you know that I linted all conda-recipes in your PR (recipes/dataframe-timeseries-mon/meta.yaml) and found it was in an excellent condition.

@Marcin-kowalczyk-polenergia
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@conda-forge-admin, please ping team python all checks have passed and the package is ready for review

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Hi! This is the friendly automated conda-forge-webservice.

I was asked to ping @conda-forge/staged-recipes and so here I am doing that.

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To help direct your pull request to the best reviewers, please mention a topic-specifc team if your recipe matches any of the following: conda-forge/help-c-cpp, conda-forge/help-cdts, conda-forge/help-go, conda-forge/help-java, conda-forge/help-julia, conda-forge/help-nodejs, conda-forge/help-perl, conda-forge/help-python, conda-forge/help-python-c, conda-forge/help-r, conda-forge/help-ruby,or conda-forge/help-rust. Thanks!

@Marcin-kowalczyk-polenergia
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@conda-forge/help-python all checks have passed and the package is ready for review whenever someone is available, thank you in advance !

… changes


I agree wit the suggested changes

Co-authored-by: Daniel Nachun <[email protected]>
…ted changes


I agree with the suggested changes

Co-authored-by: Daniel Nachun <[email protected]>
@Marcin-kowalczyk-polenergia
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thank you for suggesting the improvements, I agree about their implementation, the package can now be re-reviewed after the inclusion

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3 participants