add polars joins

polars/basic_polars_joins.py

@@ -0,0 +1,99 @@
+# /// script
+# dependencies = [
+#     "marimo",
+#     "polars==1.28.1",
+# ]
+# [tool.marimo.runtime]
+# auto_instantiate = false
+# ///
+
+import marimo
+
+__generated_with = "0.13.2"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+    import polars as pl
+    return mo, pl
+
+
+@app.cell
+def _(pl):
+    base = pl.DataFrame(
+        {"id": [1, 2, 3, 4], "name": ["Alice", "Bob", "Charlie", "Diana"]}
+    )
+
+    other = pl.DataFrame({"id": [3, 4, 5, 6], "age": [25, 32, 40, 28]})
+    return base, other
+
+
+@app.cell
+def _(base, mo, other):
+    mo.vstack(
+        [
+            mo.hstack([mo.ui.table(base, show_download=False , label='base'), mo.ui.table(other, show_download=False, label='other')], justify="center"),
+        ]
+    )
+    return
+
+
+@app.cell
+def _(mo):
+    join_options: dict = {
+        "inner (default)": "inner",
+        "left": "left",
+        "right": "right",
+        "full": "full",
+        "semi": "semi",
+        "anti": "anti",
+    }
+
+    # These are from https://docs.pola.rs/user-guide/transformations/joins/#quick-reference-table
+    descriptions: dict = {
+        "inner": "Keeps rows that matched both on the left and right.",
+        "left": "Keeps all rows from the left plus matching rows from the right. Non-matching rows from the left have their right columns filled with null.",
+        "right": "Keeps all rows from the right plus matching rows from the left. Non-matching rows from the right have their left columns filled with null.",
+        "full": "Keeps all rows from either dataframe, regardless of whether they match or not. Non-matching rows from one side have the columns from the other side filled with null.",
+        "semi": "Keeps rows from the left that have a match on the right.",
+        "anti": "Keeps rows from the left that do not have a match on the right.",
+        "join_where": "Finds all possible pairings of rows from the left and right that satisfy the given predicate(s).",
+        "join_asof": "Like a left outer join, but matches on the nearest key instead of on exact key matches.",
+        "cross": "Computes the Cartesian product of the two dataframes.",
+    }
+    dropdown = mo.ui.dropdown(
+        value="inner (default)",
+        options=join_options,
+    )
+    return descriptions, dropdown
+
+
+@app.cell
+def _(base, descriptions, dropdown, mo, other, pl):
+    result: pl.DataFrame = base.join(other, on="id", how=dropdown.value)
+    n_rows, n_columns = result.shape
+    mo.vstack(
+        [
+            dropdown,
+            mo.md(descriptions[dropdown.value]),
+            mo.md(f"rows: {n_rows} columns: {n_columns}"),
+        ]
+    )
+    return (result,)
+
+
+@app.cell
+def _(mo, result):
+    mo.vstack([result])
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+if __name__ == "__main__":
+    app.run()