patsy | Describing statistical models in Python

So in case anyone comes across similar for any reason with me encountering this probably due getting a new machine and IT doing their voodoo magic transferring my old stuff to this new machine.

Anyhow, there were a bunch of things I still needed to install after I got my new machine and i am not able to exactly pin point what caused issues from my question but in the end I was able to resolve. Follow me there below ...

Checking out my python.exe files I found 2 paths. First one added as environment variable

1. C:\Users-----\AppData\Local\Programs\Python\Python310
2. C:\Users----\AppData\Roaming\Python\Python310\

Second one not added. Adding roaming version to path variables did not solve the issue and gave additional errors instead: Fatal error in launcher: Unable to create process using '"C:\Program Files\Python310\python.exe"

So

1. I uninstalled python (done that before didnt help doing just that alone)

2. Deleted all environment variables pointing to python (here is what environment variables are just in case - https://www.computerhope.com/issues/ch000549.htm)

3. Uninstalled python extension from VS code (https://marketplace.visualstudio.com/items?itemName=ms-python.python)

4. Deleted Python folders mentioned in the two paths above

5. Then reinstalled python (clicked add to path during installation)

6. Reinstalled VS code python extension

7. Everything works now.

Best of luck

The immediate error is that the build is generating a Python 3.10 version, but when testing Conda doesn't recognize any constraint on the Python version, and creates a Python 3.9 environment.

I think the main issue is that python >=3.5 is only a valid constraint when doing noarch builds, which this is not. That is, once a package builds with a given Python version, the version must be constrained to exactly that version (up through minor). So, in this case, the package is built with Python 3.10, but it reports in its metadata that it is compatible with all versions of Python 3.5+, which simply isn't true because Conda Python packages install the modules into Python-version-specific site-packages (e.g., lib/python-3.10/site-packages/jive).

Typically, Python versions are controlled by either the --python argument given to conda-build or a matrix supplied by the conda_build_config.yaml file (see documentation on "Build variants").

Try adjusting the meta.yaml to something like

The problem in this implementation is in the fit method.

We are comparing prediction and design matrix

Build tags in you environment.yml are quite strict requirements to satisfy and most often not needed. In your case, changing the yml file to

In statsmodels formulae, you need to quote your variables (i.e. columns in your dataframe) when they contain special characters such as -. Have a look at the documentation, your term "x-axis" is interpreted as "x" - "axis". Quoting variable can be done with the Q() transformation. Make sure to quote the variable name inside with different (single/double) quotes that you use for the string:

geopandas 0.10.1

• have noted that your data is on kaggle, so start by sourcing it
• there really is only one issue shapely.geometry.MultiPoint() constructor does not work with a filtered series. Pass it a numpy array instead and it works.
• full code below, have randomly selected a point to serve as gpdPoint

You can not do this with the code you have given because you're using statsmodels.formula.api. The simplest solution I can provide is to use a quick dictionary:

Whatever is in the pip: section of a Conda environment YAML gets installed after the Conda environment is created, and is run with the pip install -U command. The -U gives Pip the permission to upgrade any existing packages if it is necessary to install the specified packages. In this particular case, the version of imblearn must be incompatible with the scikit-learn version you have selected.

Technically, you should be using imbalanced-learn not imblearn, as stated in the package description. That also means you don't even need to install from PyPI, since imbalanced-learn is available through Conda Forge.

If you require having scikit-learn=0.23 then you must use imbalanced-learn=0.7. This should be under the regular dependencies, not in the pip: section.

After a lot of research, I stumbled on to Mamba doesn't find a solution when mixing conda forge defaults and not specifying Python explicitly 1102. So I just edited nbdev.yml from