fix(examples): make csv-read-options self-contained and add output to silent examples

examples/csv-read-options.py

@@ -17,15 +17,30 @@
 
 """Example demonstrating CsvReadOptions usage."""
 
+import gzip
+import shutil
+import tempfile
+from pathlib import Path
+
 from datafusion import CsvReadOptions, SessionContext
 
+# This example is self-contained: it writes its own small CSV file, plus a
+# gzipped copy, into a temporary directory instead of expecting a
+# ``data.csv`` file that is not part of this repository.
+tmp_dir = Path(tempfile.mkdtemp())
+csv_path = tmp_dir / "data.csv"
+csv_path.write_text("a,b,c\n1,2,3\n4,5,6\n7,8,9\n")
+gz_path = tmp_dir / "data.csv.gz"
+with csv_path.open("rb") as f_in, gzip.open(gz_path, "wb") as f_out:
+    shutil.copyfileobj(f_in, f_out)
+
 # Create a SessionContext
 ctx = SessionContext()
 
 # Example 1: Using CsvReadOptions with default values
 print("Example 1: Default CsvReadOptions")
 options = CsvReadOptions()
-df = ctx.read_csv("data.csv", options=options)
+df = ctx.read_csv(str(csv_path), options=options)
 
 # Example 2: Using CsvReadOptions with custom parameters
 print("\nExample 2: Custom CsvReadOptions")
@@ -36,7 +51,7 @@
     schema_infer_max_records=1000,
     file_extension=".csv",
 )
-df = ctx.read_csv("data.csv", options=options)
+df = ctx.read_csv(str(csv_path), options=options)
 
 # Example 3: Using the builder pattern (recommended for readability)
 print("\nExample 3: Builder pattern")
@@ -49,7 +64,7 @@
     .with_truncated_rows(False)  # noqa: FBT003
     .with_newlines_in_values(True)  # noqa: FBT003
 )
-df = ctx.read_csv("data.csv", options=options)
+df = ctx.read_csv(str(csv_path), options=options)
 
 # Example 4: Advanced options
 print("\nExample 4: Advanced options")
@@ -64,18 +79,18 @@
     .with_file_compression_type("gzip")  # Read gzipped CSV
     .with_file_extension(".gz")
 )
-df = ctx.read_csv("data.csv.gz", options=options)
+df = ctx.read_csv(str(gz_path), options=options)
 
 # Example 5: Register CSV table with options
 print("\nExample 5: Register CSV table")
 options = CsvReadOptions().with_has_header(True).with_delimiter(",")  # noqa: FBT003
-ctx.register_csv("my_table", "data.csv", options=options)
+ctx.register_csv("my_table", str(csv_path), options=options)
 df = ctx.sql("SELECT * FROM my_table")
 
 # Example 6: Backward compatibility (without options)
 print("\nExample 6: Backward compatibility")
 # Still works the old way!
-df = ctx.read_csv("data.csv", has_header=True, delimiter=",")
+df = ctx.read_csv(str(csv_path), has_header=True, delimiter=",")
 
 print("\nAll examples completed!")
 print("\nFor all available options, see the CsvReadOptions documentation:")

examples/export.py

@@ -34,19 +34,24 @@
 # export to pandas dataframe
 pandas_df = df.to_pandas()
 assert pandas_df.shape == (3, 2)
+print(pandas_df)
 
 # export to PyArrow table
 arrow_table = df.to_arrow_table()
 assert arrow_table.shape == (3, 2)
+print(arrow_table)
 
 # export to Polars dataframe
 polars_df = df.to_polars()
 assert polars_df.shape == (3, 2)
+print(polars_df)
 
 # export to Python list of rows
 pylist = df.to_pylist()
 assert pylist == [{"a": 1, "b": 4}, {"a": 2, "b": 5}, {"a": 3, "b": 6}]
+print(pylist)
 
 # export to Python dictionary of columns
 pydict = df.to_pydict()
 assert pydict == {"a": [1, 2, 3], "b": [4, 5, 6]}
+print(pydict)

examples/import.py

@@ -40,18 +40,22 @@
 # Create a datafusion DataFrame from a Python list of rows
 df = ctx.from_pylist([{"a": 1, "b": 4}, {"a": 2, "b": 5}, {"a": 3, "b": 6}])
 assert type(df) is datafusion.DataFrame
+df.show()
 
 # Convert pandas DataFrame to datafusion DataFrame
 pandas_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
 df = ctx.from_pandas(pandas_df)
 assert type(df) is datafusion.DataFrame
+df.show()
 
 # Convert polars DataFrame to datafusion DataFrame
 polars_df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
 df = ctx.from_polars(polars_df)
 assert type(df) is datafusion.DataFrame
+df.show()
 
 # Convert Arrow Table to datafusion DataFrame
 arrow_table = pa.Table.from_pydict({"a": [1, 2, 3], "b": [4, 5, 6]})
 df = ctx.from_arrow(arrow_table)
 assert type(df) is datafusion.DataFrame
+df.show()

examples/python-udaf.py

@@ -63,6 +63,7 @@ def evaluate(self) -> pa.Scalar:
 )
 
 df = df.aggregate([], [my_udaf(col("a"))])
+df.show()
 
 result = df.collect()[0]
 

examples/python-udf.py

@@ -37,6 +37,7 @@ def is_null(array: pa.Array) -> pa.Array:
 df = ctx.create_dataframe([[batch]])
 
 df = df.select(is_null_arr(f.col("a")))
+df.show()
 
 result = df.collect()[0]
 

examples/query-pyarrow-data.py

@@ -35,6 +35,8 @@
     col("a") - col("b"),
 )
 
+df.show()
+
 # execute and collect the first (and only) batch
 result = df.collect()[0]
 

examples/sql-to-pandas.py

@@ -34,6 +34,7 @@
 
 # convert to Pandas
 pandas_df = df.to_pandas()
+print(pandas_df)
 
 # create a chart
 fig = pandas_df.plot(

examples/sql-using-python-udaf.py

@@ -82,5 +82,6 @@ def evaluate(self) -> pa.Scalar:
 # | 1 | 9            |
 # | 3 | 6            |
 # +---+--------------+
+result_df.show()
 assert result_df.to_pydict()["a"] == [1, 3]
 assert result_df.to_pydict()["b_aggregated"] == [9, 6]

examples/sql-using-python-udf.py

@@ -61,4 +61,5 @@ def is_null(array: pa.Array) -> pa.Array:
 # | 2 | true      |
 # | 3 | false     |
 # +---+-----------+
+result_df.show()
 assert result_df.to_pydict()["b_is_null"] == [False, True, False]

examples/substrait.py

@@ -47,3 +47,6 @@
 # Back to Substrait Plan just for demonstration purposes
 # type(substrait_plan) -> <class 'datafusion.substrait.plan'>
 substrait_plan = ss.Producer.to_substrait_plan(df_logical_plan, ctx)
+
+# Show the logical plan recovered from the Substrait round trip
+print(df_logical_plan)