python-api | A Python client for Infermedica API | REST library

As has been mentioned you could use BeautifulSoup for this.

Not sure how you want the data but the code below will create a list of dictionaries with the keys coming from the JIRA column and the values from the Type column.

You could use other methods to put the data into other types of structures.

I am afraid downloading Azure universal packages using Azure DevOps Python API is not support yet. For the NPM, Maven, Nuget and Pyton Artifacts package types there is an API which allows directly downloading of a packages, but not for Azure universal packages.

The user voice regarding this request has been submit to Microsoft Development team. You can vote it up, or create one for yourself.

Currently, you will have to download it using Azure CLI.

az artifacts universal download --organization https://dev.azure.com/Fabrikam --feed FabrikamFiber --name my-first-package --version 1.0.0 --path .

See here for more information.

Your code looks ok. Authenticating to Confluence using Basic Auth should work without generating an API token, afaik.

The 401 status definitely suggests a problem with the authentication though. The obvious reason for this would be of course wrong credentials, but I assume that you have double checked that the credentials work when interactively logging into confluence with a browser.

To get a better sense of the error, you can import logging to debug your requests and response:

This is actually directly possible with update_page(), though you are correct, it is not documented in the method's documentation.

I found in the source code that the method update_page() takes an optional argument version_comment. This is the comment that you want to set.

Extending your example:

Don't forget any first argument passed to a class method always refers to itself self.

So when you passed obj, to json.dumps(obj, ...), it passes the instance of DataFeed to json.dumps function. What you want is to pass the response.json().

LightGBM's parameter defaults are set with the expectation of moderate-sized training data, and might not work well on extremely small datasets like the one in this question.

There are two in particular that are impacting your result:

• min_data_in_leaf: minimum number of samples that must fall into a leaf node
• min_sum_hessian_in_leaf: basically, the minimum contribution to the loss function for one leaf node

Setting these to the lowest possible values can force LightGBM to overfit to such a small dataset.

The add_vline function uses recently-added features in the underlying rendering engine, Plotly.js, meaning you need a recent version of Plotly.js wherever you are using Plotly.py. In practice this means installing the latest jupyterlab-plotly extension in JupyterLab, the latest dash/dash_core_components in Dash, or waiting until VSCode and/or Streamlit update their onboard copies of Plotly.js, if you’re seeing this problem in those environments.

More information can be found here: Troubleshooting | Python | Plotly

The libsbml API is designed to directly mimic the structure of SBML itself. So, once you have the model, you get the reactions from the model, and once you have a reaction, you get the reaction's modifiers:

You want users to be able to generate three things:

• the field (can't be parameterizeda, and it looks like those correspond to column names in your DB)
• the comparator (can't be parameterized)
• the value for the field (which can be parameterized)

Fortunately, there's a very limited set of values for the field and the comparator. Can you build a list of those and then validate them yourself before concatenating them to the query?

Something like: