feat: expose DataFrame.parse_sql_expr

python/datafusion/dataframe.py

@@ -482,6 +482,28 @@ def filter(self, *predicates: Expr) -> DataFrame:
             df = df.filter(ensure_expr(p))
         return DataFrame(df)
 
+    def parse_sql_expr(self, expr: str) -> Expr:
+        """Creates logical expression from a SQL query text.
+
+        The expression is created and processed against the current schema.
+
+        Example::
+
+            from datafusion import col, lit
+            df.parse_sql_expr("a > 1")
+
+            should produce:
+
+            col("a") > lit(1)
+
+        Args:
+            expr: Expression string to be converted to datafusion expression
+
+        Returns:
+            Logical expression .
+        """
+        return Expr(self.df.parse_sql_expr(expr))
+
     def with_column(self, name: str, expr: Expr) -> DataFrame:
         """Add an additional column to the DataFrame.
 

python/tests/test_dataframe.py

@@ -274,6 +274,31 @@ def test_filter(df):
     assert result.column(2) == pa.array([5])
 
 
+def test_parse_sql_expr(df):
+    df1 = df.filter(df.parse_sql_expr("a > 2")).select(
+        column("a") + column("b"),
+        column("a") - column("b"),
+    )
+
+    # execute and collect the first (and only) batch
+    result = df1.collect()[0]
+
+    assert result.column(0) == pa.array([9])
+    assert result.column(1) == pa.array([-3])
+
+    df.show()
+    # verify that if there is no filter applied, internal dataframe is unchanged
+    df2 = df.filter()
+    assert df.df == df2.df
+
+    df3 = df.filter(df.parse_sql_expr("a > 1"), df.parse_sql_expr("b != 6"))
+    result = df3.collect()[0]
+
+    assert result.column(0) == pa.array([2])
+    assert result.column(1) == pa.array([5])
+    assert result.column(2) == pa.array([5])
+
+
 def test_show_empty(df, capsys):
     df_empty = df.filter(column("a") > literal(3))
     df_empty.show()

src/dataframe.rs

@@ -454,6 +454,14 @@ impl PyDataFrame {
         Ok(Self::new(df))
     }
 
+    fn parse_sql_expr(&self, expr: PyBackedStr) -> PyDataFusionResult<PyExpr> {
+        self.df
+            .as_ref()
+            .parse_sql_expr(&expr)
+            .map(|e| PyExpr::from(e))
+            .map_err(PyDataFusionError::from)
+    }
+
     fn with_column(&self, name: &str, expr: PyExpr) -> PyDataFusionResult<Self> {
         let df = self.df.as_ref().clone().with_column(name, expr.into())?;
         Ok(Self::new(df))