WIP for blog post

examples/due_date_probabilities/README.md

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+# Predicting due date using Hamilton
+
+This is a fun example of a simple script in a jupyter notebook using Hamilton.
+
+The goal is to predict the probability of delivery for an expecting mother, given:
+1. The start date of the pregnancy
+2. The current date
+
+We want to answer the questions:
+1. What is the probability that she will give birth on date X?
+2. What is the probability that she will give birth before date X?
+
+You can follow along in the [jupyter notebook](./notebook.ipynb) -- the entire dataflow is written/documented there.
+
+For a guideline of using Hamilton in a jupyter notebook, see this [page](https://hamilton.dagworks.io/en/latest/how-tos/use-in-jupyter-notebook/)

examples/due_date_probabilities/base_dates.py

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+import datetime
+
+import pandas as pd
+
+
+def due_date(start_date: datetime.datetime) -> datetime.datetime:
+    """The due date is start_date + 40 weeks. Start date is the date of the expecting mother's last period"""
+    return start_date + datetime.timedelta(weeks=40)
+
+
+def possible_dates(
+    due_date: datetime.datetime,
+    buffer_before_due_date: int = 8 * 7,
+    buffer_after_due_date: int = 4 * 7,
+) -> pd.Series:
+    """Gets all the reasonably possible dates (-8 weeks, + 4 weeks) of delivery"""
+    idx = pd.date_range(
+        due_date - datetime.timedelta(days=buffer_before_due_date),
+        due_date + datetime.timedelta(days=buffer_after_due_date),
+    )
+    return pd.Series(data=idx, index=idx)