| # 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 |
| # programmatic code to load modules: |
| # module_name = "my_functions" |
| # my_functions = importlib.import_module(module_name) |
| # or import module(s) directly: |
| import my_functions |
| |
| dr = driver.Driver(initial_columns, my_functions) # can pass in multiple modules |
| # we need to specify what we want in the final dataframe. These can be string names, or function references. |
| output_columns = [ |
| "spend", |
| "signups", |
| my_functions.avg_3wk_spend, # could just pass "avg_3wk_spend" here |
| my_functions.spend_per_signup, # could just pass "spend_per_signup" here |
| my_functions.spend_zero_mean_unit_variance, # could just pass "spend_zero_mean_unit_variance" here |
| ] |
| # 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", {"format": "png"}) |
| # dr.display_all_functions("./my_full_dag.dot", {"format": "png"}) |
