diff --git a/pyiceberg/io/pyarrow.py b/pyiceberg/io/pyarrow.py
index 9ab1981069..23aec2d35e 100644
--- a/pyiceberg/io/pyarrow.py
+++ b/pyiceberg/io/pyarrow.py
@@ -2397,8 +2397,8 @@ def data_file_statistics_from_parquet_metadata(
     split_offsets.sort()
 
     for field_id in invalidate_col:
-        del col_aggs[field_id]
-        del null_value_counts[field_id]
+        col_aggs.pop(field_id, None)
+        null_value_counts.pop(field_id, None)
 
     return DataFileStatistics(
         record_count=parquet_metadata.num_rows,
diff --git a/tests/io/test_pyarrow_stats.py b/tests/io/test_pyarrow_stats.py
index 41f1432dbf..788891711e 100644
--- a/tests/io/test_pyarrow_stats.py
+++ b/tests/io/test_pyarrow_stats.py
@@ -81,7 +81,9 @@ class TestStruct:
     y: Optional[float]
 
 
-def construct_test_table() -> Tuple[pq.FileMetaData, Union[TableMetadataV1, TableMetadataV2]]:
+def construct_test_table(
+    write_statistics: Union[bool, List[str]] = True,
+) -> Tuple[pq.FileMetaData, Union[TableMetadataV1, TableMetadataV2]]:
     table_metadata = {
         "format-version": 2,
         "location": "s3://bucket/test/location",
@@ -169,7 +171,9 @@ def construct_test_table() -> Tuple[pq.FileMetaData, Union[TableMetadataV1, Tabl
     metadata_collector: List[Any] = []
 
     with pa.BufferOutputStream() as f:
-        with pq.ParquetWriter(f, table.schema, metadata_collector=metadata_collector) as writer:
+        with pq.ParquetWriter(
+            f, table.schema, metadata_collector=metadata_collector, write_statistics=write_statistics
+        ) as writer:
             writer.write_table(table)
 
     return metadata_collector[0], table_metadata
@@ -681,6 +685,41 @@ def test_stats_types(table_schema_nested: Schema) -> None:
     ]
 
 
+def test_read_missing_statistics() -> None:
+    # write statistics for only for "strings" column
+    metadata, table_metadata = construct_test_table(write_statistics=["strings"])
+
+    # expect only "strings" column to have statistics in metadata
+    # and all other columns to have no statistics
+    for r in range(metadata.num_row_groups):
+        for pos in range(metadata.num_columns):
+            if metadata.row_group(r).column(pos).path_in_schema == "strings":
+                assert metadata.row_group(r).column(pos).is_stats_set is True
+                assert metadata.row_group(r).column(pos).statistics is not None
+            else:
+                assert metadata.row_group(r).column(pos).is_stats_set is False
+                assert metadata.row_group(r).column(pos).statistics is None
+
+    schema = get_current_schema(table_metadata)
+    statistics = data_file_statistics_from_parquet_metadata(
+        parquet_metadata=metadata,
+        stats_columns=compute_statistics_plan(schema, table_metadata.properties),
+        parquet_column_mapping=parquet_path_to_id_mapping(schema),
+    )
+
+    datafile = DataFile(**statistics.to_serialized_dict())
+
+    # expect only "strings" column values to be reflected in the
+    # upper_bound, lower_bound and null_value_counts props of datafile
+    string_col_idx = 1
+    assert len(datafile.lower_bounds) == 1
+    assert datafile.lower_bounds[string_col_idx].decode() == "aaaaaaaaaaaaaaaa"
+    assert len(datafile.upper_bounds) == 1
+    assert datafile.upper_bounds[string_col_idx].decode() == "zzzzzzzzzzzzzzz{"
+    assert len(datafile.null_value_counts) == 1
+    assert datafile.null_value_counts[string_col_idx] == 1
+
+
 # This is commented out for now because write_to_dataset drops the partition
 # columns making it harder to calculate the mapping from the column index to
 # datatype id