From 25904588d27ae121ebd9dfe4410ae89683d52604 Mon Sep 17 00:00:00 2001
From: ion-elgreco <15728914+ion-elgreco@users.noreply.github.com>
Date: Sat, 30 Dec 2023 15:57:23 +0100
Subject: [PATCH] expose large_dtype

---
 python/deltalake/table.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/python/deltalake/table.py b/python/deltalake/table.py
index 5adeaaa9dc..76b1dd4f49 100644
--- a/python/deltalake/table.py
+++ b/python/deltalake/table.py
@@ -808,6 +808,7 @@ def merge(
         target_alias: Optional[str] = None,
         error_on_type_mismatch: bool = True,
         writer_properties: Optional[WriterProperties] = None,
+        large_dtypes: bool = True,
     ) -> "TableMerger":
         """Pass the source data which you want to merge on the target delta table, providing a
         predicate in SQL query like format. You can also specify on what to do when the underlying data types do not
@@ -820,6 +821,7 @@ def merge(
             target_alias: Alias for the target table
             error_on_type_mismatch: specify if merge will return error if data types are mismatching :default = True
             writer_properties: Pass writer properties to the Rust parquet writer
+            large_dtypes: If True, the data schema is kept in large_dtypes.
 
         Returns:
             TableMerger: TableMerger Object
@@ -835,16 +837,16 @@ def merge(
         )
 
         if isinstance(source, pyarrow.RecordBatchReader):
-            source = convert_pyarrow_recordbatchreader(source, large_dtypes=True)
+            source = convert_pyarrow_recordbatchreader(source, large_dtypes)
         elif isinstance(source, pyarrow.RecordBatch):
-            source = convert_pyarrow_recordbatch(source, large_dtypes=True)
+            source = convert_pyarrow_recordbatch(source, large_dtypes)
         elif isinstance(source, pyarrow.Table):
-            source = convert_pyarrow_table(source, large_dtypes=True)
+            source = convert_pyarrow_table(source, large_dtypes)
         elif isinstance(source, ds.Dataset):
-            source = convert_pyarrow_dataset(source, large_dtypes=True)
+            source = convert_pyarrow_dataset(source, large_dtypes)
         elif isinstance(source, pandas.DataFrame):
             source = convert_pyarrow_table(
-                pyarrow.Table.from_pandas(source), large_dtypes=True
+                pyarrow.Table.from_pandas(source), large_dtypes
             )
         else:
             raise TypeError(