timsaucer commented on code in PR #1016:
URL:
https://github.com/apache/datafusion-python/pull/1016#discussion_r1985040308
##########
src/dataframe.rs:
##########
@@ -50,9 +52,79 @@ use crate::{
expr::{sort_expr::PySortExpr, PyExpr},
};
+// https://github.com/apache/datafusion-python/pull/1016#discussion_r1983239116
+// - we have not decided on the table_provider approach yet
+// this is an interim implementation
+#[pyclass(name = "TableProvider", module = "datafusion")]
+pub struct PyTableProvider {
+ provider: Arc<dyn TableProvider>,
+}
+
+impl PyTableProvider {
+ pub fn new(provider: Arc<dyn TableProvider>) -> Self {
+ Self { provider }
+ }
+
+ pub fn as_table(&self) -> PyTable {
+ let table_provider: Arc<dyn TableProvider> = self.provider.clone();
+ PyTable::new(table_provider)
+ }
+}
+
/// A PyDataFrame is a representation of a logical plan and an API to compose
statements.
/// Use it to build a plan and `.collect()` to execute the plan and collect
the result.
/// The actual execution of a plan runs natively on Rust and Arrow on a
multi-threaded environment.
+///
+/// # Methods
+///
+/// - `new`: Creates a new PyDataFrame.
+/// - `__getitem__`: Enable selection for `df[col]`, `df[col1, col2, col3]`,
and `df[[col1, col2, col3]]`.
+/// - `__repr__`: Returns a string representation of the DataFrame.
+/// - `_repr_html_`: Returns an HTML representation of the DataFrame.
+/// - `describe`: Calculate summary statistics for a DataFrame.
+/// - `schema`: Returns the schema from the logical plan.
+/// - `into_view`: Convert this DataFrame into a Table that can be used in
register_table. We have not finalized on PyTableProvider approach yet.
+/// - `select_columns`: Select columns from the DataFrame.
+/// - `select`: Select expressions from the DataFrame.
+/// - `drop`: Drop columns from the DataFrame.
+/// - `filter`: Filter the DataFrame based on a predicate.
+/// - `with_column`: Add a new column to the DataFrame.
+/// - `with_columns`: Add multiple new columns to the DataFrame.
+/// - `with_column_renamed`: Rename a column in the DataFrame.
+/// - `aggregate`: Aggregate the DataFrame based on group by and aggregation
expressions.
+/// - `sort`: Sort the DataFrame based on expressions.
+/// - `limit`: Limit the number of rows in the DataFrame.
+/// - `collect`: Executes the plan, returning a list of `RecordBatch`es.
Review Comment:
I don't think we need this whole section. The methods of the class should
auto populate into the `help` and online documentation. Do you think this is
necessary? It's another thing we'd have to manually maintain over time.
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