Sample dummy models for testing. Golden rule is that python models have to always return a dataframe.
# sf_table.py
import pandas as pd
def model(dbt, session):
{% macro trange_join(left_model, row_key_left, right_model, row_key_right, merge_key, series_key, from_at, to_at) %} | |
-- Assumptions: | |
-- Within a series key, there can be no overlapping ranges. | |
-- Overlaping ranges must be resolved prior to using this macro. | |
-- The row key represents a set of attributes from a model. It is typically a hash of those | |
-- attributes. The user is expected to join the results of this macro back to the | |
-- original data to get the full attribute set. | |
Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.
In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.
Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j