Support partial deletes

mkdocs/docs/api.md

@@ -331,12 +331,25 @@ df = pa.Table.from_pylist(
 table.append(df)
 ```
 
-<!-- prettier-ignore-start -->
+You can delete some of the data from the table by calling `tbl.delete()` with a desired `delete_filter`.
+
+```python
+tbl.delete(delete_filter="city == 'Paris'")
+```
 
-!!! example "Under development"
-    Writing using PyIceberg is still under development. Support for [partial overwrites](https://github.com/apache/iceberg-python/issues/268) and writing to [partitioned tables](https://github.com/apache/iceberg-python/issues/208) is planned and being worked on.
+In the above example, any records where the city field value equals to `Paris` will be deleted.
+Running `tbl.scan().to_arrow()` will now yield:
 
-<!-- prettier-ignore-end -->
+```
+pyarrow.Table
+city: string
+lat: double
+long: double
+----
+city: [["Amsterdam","San Francisco","Drachten"],["Groningen"]]
+lat: [[52.371807,37.773972,53.11254],[53.21917]]
+long: [[4.896029,-122.431297,6.0989],[6.56667]]
+```
 
 ## Inspecting tables
 

pyiceberg/io/pyarrow.py

@@ -31,6 +31,7 @@
 import logging
 import os
 import re
+import uuid
 from abc import ABC, abstractmethod
 from concurrent.futures import Future
 from copy import copy
@@ -126,7 +127,6 @@
     visit,
     visit_with_partner,
 )
-from pyiceberg.table import PropertyUtil, TableProperties, WriteTask
 from pyiceberg.table.metadata import TableMetadata
 from pyiceberg.table.name_mapping import NameMapping
 from pyiceberg.transforms import TruncateTransform
@@ -159,7 +159,7 @@
 from pyiceberg.utils.truncate import truncate_upper_bound_binary_string, truncate_upper_bound_text_string
 
 if TYPE_CHECKING:
-    from pyiceberg.table import FileScanTask
+    from pyiceberg.table import FileScanTask, WriteTask
 
 logger = logging.getLogger(__name__)
 
@@ -1563,6 +1563,8 @@ class PyArrowStatisticsCollector(PreOrderSchemaVisitor[List[StatisticsCollector]
     _default_mode: str
 
     def __init__(self, schema: Schema, properties: Dict[str, str]):
+        from pyiceberg.table import TableProperties
+
         self._schema = schema
         self._properties = properties
         self._default_mode = self._properties.get(
@@ -1598,6 +1600,8 @@ def map(
         return k + v
 
     def primitive(self, primitive: PrimitiveType) -> List[StatisticsCollector]:
+        from pyiceberg.table import TableProperties
+
         column_name = self._schema.find_column_name(self._field_id)
         if column_name is None:
             return []
@@ -1895,6 +1899,8 @@ def data_file_statistics_from_parquet_metadata(
 
 
 def write_file(io: FileIO, table_metadata: TableMetadata, tasks: Iterator[WriteTask]) -> Iterator[DataFile]:
+    from pyiceberg.table import PropertyUtil, TableProperties
+
     parquet_writer_kwargs = _get_parquet_writer_kwargs(table_metadata.properties)
     row_group_size = PropertyUtil.property_as_int(
         properties=table_metadata.properties,
@@ -2005,6 +2011,8 @@ def parquet_files_to_data_files(io: FileIO, table_metadata: TableMetadata, file_
 
 
 def _get_parquet_writer_kwargs(table_properties: Properties) -> Dict[str, Any]:
+    from pyiceberg.table import PropertyUtil, TableProperties
+
     for key_pattern in [
         TableProperties.PARQUET_ROW_GROUP_SIZE_BYTES,
         TableProperties.PARQUET_PAGE_ROW_LIMIT,
@@ -2042,3 +2050,55 @@ def _get_parquet_writer_kwargs(table_properties: Properties) -> Dict[str, Any]:
             default=TableProperties.PARQUET_PAGE_ROW_LIMIT_DEFAULT,
         ),
     }
+
+
+def _dataframe_to_data_files(
+    table_metadata: TableMetadata,
+    df: pa.Table,
+    io: FileIO,
+    write_uuid: Optional[uuid.UUID] = None,
+    counter: Optional[itertools.count[int]] = None,
+) -> Iterable[DataFile]:
+    """Convert a PyArrow table into a DataFile.
+
+    Returns:
+        An iterable that supplies datafiles that represent the table.
+    """
+    from pyiceberg.table import PropertyUtil, TableProperties, WriteTask
+
+    counter = counter or itertools.count(0)
+    write_uuid = write_uuid or uuid.uuid4()
+    target_file_size: int = PropertyUtil.property_as_int(  # type: ignore  # The property is set with non-None value.
+        properties=table_metadata.properties,
+        property_name=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES,
+        default=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT,
+    )
+
+    if table_metadata.spec().is_unpartitioned():
+        yield from write_file(
+            io=io,
+            table_metadata=table_metadata,
+            tasks=iter([
+                WriteTask(write_uuid=write_uuid, task_id=next(counter), record_batches=batches, schema=table_metadata.schema())
+                for batches in bin_pack_arrow_table(df, target_file_size)
+            ]),
+        )
+    else:
+        from pyiceberg.table import _determine_partitions
+
+        partitions = _determine_partitions(spec=table_metadata.spec(), schema=table_metadata.schema(), arrow_table=df)
+        yield from write_file(
+            io=io,
+            table_metadata=table_metadata,
+            tasks=iter([
+                WriteTask(
+                    write_uuid=write_uuid,
+                    task_id=next(counter),
+                    record_batches=batches,
+                    partition_key=partition.partition_key,
+                    schema=table_metadata.schema(),
+                )
+                for partition in partitions
+                for batches in bin_pack_arrow_table(partition.arrow_table_partition, target_file_size)
+            ]),
+        )

pyiceberg/manifest.py

@@ -341,7 +341,7 @@ class DataFile(Record):
     split_offsets: Optional[List[int]]
     equality_ids: Optional[List[int]]
     sort_order_id: Optional[int]
-    spec_id: Optional[int]
+    spec_id: int
 
     def __setattr__(self, name: str, value: Any) -> None:
         """Assign a key/value to a DataFile."""