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src/context.rs

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@@ -48,7 +48,7 @@ use crate::utils::{get_tokio_runtime, validate_pycapsule, wait_for_future};
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use datafusion::arrow::datatypes::{DataType, Schema, SchemaRef};
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use datafusion::arrow::pyarrow::PyArrowType;
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use datafusion::arrow::record_batch::RecordBatch;
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use datafusion::catalog_common::TableReference;
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use datafusion::common::TableReference;
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use datafusion::common::{exec_err, ScalarValue};
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use datafusion::datasource::file_format::file_compression_type::FileCompressionType;
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use datafusion::datasource::file_format::parquet::ParquetFormat;

src/dataframe.rs

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@@ -546,7 +546,7 @@ impl PyDataFrame {
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/// Collect the batches and pass to Arrow Table
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fn to_arrow_table(&self, py: Python<'_>) -> PyResult<PyObject> {
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let batches = self.collect(py)?.to_object(py);
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let schema: PyObject = self.schema().into_py(py);
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let schema: PyObject = self.schema().into_pyobject(py)?.to_object(py);
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// Instantiate pyarrow Table object and use its from_batches method
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let table_class = py.import_bound("pyarrow")?.getattr("Table")?;

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