| # Classic Hamilton Hello World |
| |
| In this example we show you how to create a simple hello world dataflow that |
| creates a polars dataframe as a result. It performs a series of transforms on the |
| input to create columns that appear in the output. |
| |
| File organization: |
| |
| * `my_functions.py` houses the logic that we want to compute. |
| Note (1) how the functions are named, and what input |
| parameters they require. That is how we create a DAG modeling the dataflow we want to happen. |
| * `my_script.py` houses how to get Hamilton to create the DAG, specifying that we want a polars dataframe and |
| exercise it with some inputs. |
| |
| To run things: |
| ```bash |
| > python my_script.py |
| ``` |
| |
| # Visualizing Execution |
| Here is the graph of execution - which should look the same as the pandas example: |
| |
|  |
| |
| # Caveat with Polars |
| There is one major caveat with Polars to be aware of: THERE IS NO INDEX IN POLARS LIKE THERE IS WITH PANDAS. |
| |
| What this means is that when you tell Hamilton to execute and return a polars dataframe if you are using the |
| [provided results builder](https://github.com/dagworks-inc/hamilton/blob/sf-hamilton-1.14.1/hamilton/plugins/h_polars.py#L8), i.e. `hamilton.plugins.h_polars.PolarsResultsBuilder`, then you will have to |
| ensure the row order matches the order you expect for all the outputs you request. E.g. if you do a filter, or a sort, |
| or a join, or a groupby, you will have to ensure that when you ask Hamilton to materialize an output that it's in the |
| order you expect. |
| |
| If you have questions, or need help with this example, |
| join us on [slack](https://join.slack.com/t/hamilton-opensource/shared_invite/zt-1bjs72asx-wcUTgH7q7QX1igiQ5bbdcg), and we'll try to help! |
| |
| Otherwise if you have ideas on how to better make Hamilton work with Polars, please open an issue or start a discussion! |
