| import pandas as pd |
| |
| from hamilton.function_modifiers import tag |
| |
| # --- creating the dataset |
| |
| |
| @tag(owner="data-science", importance="production", artifact="training_set") |
| def training_set_v1( |
| pclass: pd.Series, |
| age: pd.Series, |
| fare: pd.Series, |
| cabin_category: pd.Series, |
| sex_category: pd.Series, |
| embarked_category: pd.Series, |
| family: pd.Series, |
| ) -> pd.DataFrame: |
| """Creates the dataset -- this is one way to do it. Explicitly make a function. |
| |
| :param pclass: |
| :param age: |
| :param fare: |
| :param cabin_category: |
| :param sex_category: |
| :param embarked_category: |
| :param family: |
| :return: a data set to use for model building. |
| """ |
| df = pd.DataFrame( |
| { |
| "pclass": pclass, |
| "age": age, |
| "fare": fare, |
| "cabin_category": cabin_category, |
| "sex_category": sex_category, |
| "embarked_category": embarked_category, |
| "family": family, |
| } |
| ) |
| df.fillna(0, inplace=True) |
| return df |
