| import importlib |
| import logging |
| import sys |
| |
| import pandas as pd |
| |
| from hamilton import driver |
| |
| logging.basicConfig(stream=sys.stdout) |
| initial_columns = { # load from actuals or wherever -- this is our initial data we use as input. |
| # Note: these values don't have to be all series, they could be a scalar. |
| "signups": pd.Series([1, 10, 50, 100, 200, 400]), |
| "spend": pd.Series([10, 10, 20, 40, 40, 50]), |
| } |
| # we need to tell hamilton where to load function definitions from |
| module_name = "my_functions" |
| module = importlib.import_module(module_name) |
| dr = driver.Driver(initial_columns, module) # can pass in multiple modules |
| # we need to specify what we want in the final dataframe. |
| output_columns = [ |
| "spend", |
| "signups", |
| "avg_3wk_spend", |
| "spend_per_signup", |
| "spend_zero_mean_unit_variance", |
| ] |
| # let's create the dataframe! |
| df = dr.execute(output_columns) |
| print(df.to_string()) |
| |
| # To visualize do `pip install sf-hamilton[visualization]` if you want these to work |
| # dr.visualize_execution(output_columns, './my_dag.dot', {}) |
| # dr.display_all_functions('./my_full_dag.dot') |
