| import pandas as pd |
| |
| from hamilton.function_modifiers import does, extract_columns, parameterize, source, value |
| |
| |
| def _sum_series(**series): |
| return sum(series.values()) |
| |
| |
| @extract_columns( |
| "facebook_spend", |
| "twitter_spend", |
| "tv_spend", |
| "youtube_spend", |
| "radio_spend", |
| "billboards_spend", |
| "womens_churn", |
| "mens_churn", |
| "womens_signups", |
| "mens_signups", |
| ) |
| def joined_data(spend: pd.DataFrame, signups: pd.DataFrame, churn: pd.DataFrame) -> pd.DataFrame: |
| spend = spend.set_index("date").rename(columns=lambda col: col + "_spend") |
| churn = churn.set_index("date").rename(columns=lambda col: col + "_churn") |
| signups = signups.set_index("date").rename(columns=lambda col: col + "_signups") |
| return pd.concat([spend, churn, signups], axis=1) |
| |
| |
| @does(_sum_series) |
| def total_marketing_spend( |
| facebook_spend: pd.Series, |
| twitter_spend: pd.Series, |
| tv_spend: pd.Series, |
| youtube_spend: pd.Series, |
| radio_spend: pd.Series, |
| billboards_spend: pd.Series, |
| ) -> pd.Series: |
| pass |
| |
| |
| @does(_sum_series) |
| def total_signups(mens_signups: pd.Series, womens_signups: pd.Series) -> pd.Series: |
| pass |
| |
| |
| @does(_sum_series) |
| def total_churn(mens_churn: pd.Series, womens_churn: pd.Series) -> pd.Series: |
| pass |
| |
| |
| def total_customers(total_signups: pd.Series, total_churn: pd.Series) -> pd.Series: |
| customer_deltas = total_signups + total_churn |
| return customer_deltas.cumsum() |
| |
| |
| def acquisition_cost(total_marketing_spend: pd.Series, total_signups: pd.Series) -> pd.Series: |
| return total_marketing_spend / total_signups |
| |
| |
| @parameterize( |
| twitter_spend_smoothed={"lookback_days": value(7), "spend": source("twitter_spend")}, |
| facebook_spend_smoothed={"lookback_days": value(7), "spend": source("facebook_spend")}, |
| radio_spend_smoothed={"lookback_days": value(21), "spend": source("radio_spend")}, |
| tv_spend_smoothed={"lookback_days": value(21), "spend": source("tv_spend")}, |
| billboards_spend_smoothed={"lookback_days": value(7), "spend": source("billboards_spend")}, |
| youtube_spend_smoothed={"lookback_days": value(7), "spend": source("twitter_spend")}, |
| ) |
| def spend_smoothed(lookback_days: int, spend: pd.Series) -> pd.Series: |
| """{spend} smoothed by {lookback_days}. Might want to smooth different ad spends differently, |
| figuring that it takes different amounts of time to get to the customer. A cheap hack at determining |
| auto-correlation of a series -- this should be a parameter in a model, |
| but this is to demonstrate the framework |
| |
| :param lookback_days: Days to smooth over |
| :param spend: Spend source |
| :return: |
| """ |
| return spend.rolling(window=lookback_days).mean().fillna(0) |
