[CHORE] Populate previews only when show() or __repr__() is called

daft/dataframe/dataframe.py

@@ -85,6 +85,7 @@ def __init__(self, builder: LogicalPlanBuilder) -> None:
         self.__builder = builder
         self._result_cache: Optional[PartitionCacheEntry] = None
         self._preview = DataFramePreview(preview_partition=None, dataframe_num_rows=None)
+        self._num_preview_rows = get_context().daft_execution_config.num_preview_rows
 
     @property
     def _builder(self) -> LogicalPlanBuilder:
@@ -225,13 +226,33 @@ def iter_partitions(self) -> Iterator[Union[MicroPartition, "RayObjectRef"]]:
             for result in results_iter:
                 yield result.partition()
 
+    def _populate_preview(self) -> None:
+        """Populates the preview of the DataFrame, if it is not already populated."""
+        if self._result is None:
+            return
+
+        preview_partition_invalid = (
+            self._preview.preview_partition is None or len(self._preview.preview_partition) < self._num_preview_rows
+        )
+        if preview_partition_invalid:
+            preview_parts = self._result._get_preview_vpartition(self._num_preview_rows)
+            preview_results = LocalPartitionSet({i: part for i, part in enumerate(preview_parts)})
+
+            preview_partition = preview_results._get_merged_vpartition()
+            self._preview = DataFramePreview(
+                preview_partition=preview_partition,
+                dataframe_num_rows=len(self),
+            )
+
     @DataframePublicAPI
     def __repr__(self) -> str:
+        self._populate_preview()
         display = DataFrameDisplay(self._preview, self.schema())
         return display.__repr__()
 
     @DataframePublicAPI
     def _repr_html_(self) -> str:
+        self._populate_preview()
         display = DataFrameDisplay(self._preview, self.schema())
         return display._repr_html_()
 
@@ -305,30 +326,7 @@ def _from_tables(cls, *parts: MicroPartition) -> "DataFrame":
         df._result_cache = cache_entry
 
         # build preview
-        num_preview_rows = context.daft_execution_config.num_preview_rows
-        dataframe_num_rows = len(df)
-        if dataframe_num_rows > num_preview_rows:
-            need = num_preview_rows
-            preview_parts = []
-            for part in parts:
-                part_len = len(part)
-                if part_len >= need:  # if this part has enough rows, take what we need and break
-                    preview_parts.append(part.slice(0, need))
-                    break
-                else:  # otherwise, take the whole part and keep going
-                    need -= part_len
-                    preview_parts.append(part)
-
-            preview_results = LocalPartitionSet({i: part for i, part in enumerate(preview_parts)})
-        else:
-            preview_results = result_pset
-
-        # set preview
-        preview_partition = preview_results._get_merged_vpartition()
-        df._preview = DataFramePreview(
-            preview_partition=preview_partition,
-            dataframe_num_rows=dataframe_num_rows,
-        )
+        df._populate_preview()
         return df
 
     ###
@@ -1129,26 +1127,10 @@ def collect(self, num_preview_rows: Optional[int] = 8) -> "DataFrame":
 
         assert self._result is not None
         dataframe_len = len(self._result)
-        requested_rows = dataframe_len if num_preview_rows is None else num_preview_rows
-
-        # Build a DataFramePreview and cache it if necessary
-        preview_partition_invalid = (
-            self._preview.preview_partition is None or len(self._preview.preview_partition) < requested_rows
-        )
-        if preview_partition_invalid:
-            preview_df = self
-            if num_preview_rows is not None:
-                preview_df = preview_df.limit(num_preview_rows)
-            preview_df._materialize_results()
-            preview_results = preview_df._result
-            assert preview_results is not None
-
-            preview_partition = preview_results._get_merged_vpartition()
-            self._preview = DataFramePreview(
-                preview_partition=preview_partition,
-                dataframe_num_rows=dataframe_len,
-            )
-
+        if num_preview_rows is not None:
+            self._num_preview_rows = num_preview_rows
+        else:
+            self._num_preview_rows = dataframe_len
         return self
 
     def _construct_show_display(self, n: int) -> "DataFrameDisplay":

daft/runners/partitioning.py

@@ -209,6 +209,9 @@
     def _get_merged_vpartition(self) -> MicroPartition:
         raise NotImplementedError()
 
+    def _get_preview_vpartition(self, num_rows: int) -> list[MicroPartition]:
+        raise NotImplementedError()
+
     def to_pydict(self) -> dict[str, list[Any]]:
         """Retrieves all the data in a PartitionSet as a Python dictionary. Values are the raw data from each Block."""
         merged_partition = self._get_merged_vpartition()

daft/runners/pyrunner.py

@@ -51,6 +51,19 @@ def _get_merged_vpartition(self) -> MicroPartition:
         assert ids_and_partitions[-1][0] + 1 == len(ids_and_partitions)
         return MicroPartition.concat([part for id, part in ids_and_partitions])
 
+    def _get_preview_vpartition(self, num_rows: int) -> list[MicroPartition]:
+        ids_and_partitions = self.items()
+        preview_parts = []
+        for _, part in ids_and_partitions:
+            part_len = len(part)
+            if part_len >= num_rows:  # if this part has enough rows, take what we need and break
+                preview_parts.append(part.slice(0, num_rows))
+                break
+            else:  # otherwise, take the whole part and keep going
+                num_rows -= part_len
+                preview_parts.append(part)
+        return preview_parts
+
     def get_partition(self, idx: PartID) -> MicroPartition:
         return self._partitions[idx]
 

daft/runners/ray_runner.py

@@ -151,6 +151,20 @@ def _get_merged_vpartition(self) -> MicroPartition:
         all_partitions = ray.get([part for id, part in ids_and_partitions])
         return MicroPartition.concat(all_partitions)
 
+    def _get_preview_vpartition(self, num_rows: int) -> list[MicroPartition]:
+        ids_and_partitions = self.items()
+        preview_parts = []
+        for _, part in ids_and_partitions:
+            part = ray.get(part)
+            part_len = len(part)
+            if part_len >= num_rows:  # if this part has enough rows, take what we need and break
+                preview_parts.append(part.slice(0, num_rows))
+                break
+            else:  # otherwise, take the whole part and keep going
+                num_rows -= part_len
+                preview_parts.append(part)
+        return preview_parts
+
     def to_ray_dataset(self) -> RayDataset:
         if not _RAY_FROM_ARROW_REFS_AVAILABLE:
             raise ImportError(

tests/cookbook/test_write.py

@@ -21,6 +21,8 @@ def test_parquet_write(tmp_path):
     assert_df_equals(df.to_pandas(), read_back_pd_df)
 
     assert len(pd_df) == 1
+    assert pd_df._preview.preview_partition is None
+    pd_df.__repr__()
     assert len(pd_df._preview.preview_partition) == 1
 
 
@@ -33,6 +35,8 @@ def test_parquet_write_with_partitioning(tmp_path):
     assert_df_equals(df.to_pandas(), read_back_pd_df)
 
     assert len(pd_df) == 5
+    assert pd_df._preview.preview_partition is None
+    pd_df.__repr__()
     assert len(pd_df._preview.preview_partition) == 5
 
 
@@ -41,6 +45,8 @@ def test_empty_parquet_write_without_partitioning(tmp_path):
     df = df.where(daft.lit(False))
     output_files = df.write_parquet(tmp_path)
     assert len(output_files) == 0
+    assert output_files._preview.preview_partition is None
+    output_files.__repr__()
     assert len(output_files._preview.preview_partition) == 0
 
 
@@ -49,6 +55,8 @@ def test_empty_parquet_write_with_partitioning(tmp_path):
     df = df.where(daft.lit(False))
     output_files = df.write_parquet(tmp_path, partition_cols=["Borough"])
     assert len(output_files) == 0
+    assert output_files._preview.preview_partition is None
+    output_files.__repr__()
     assert len(output_files._preview.preview_partition) == 0
 
 
@@ -69,6 +77,8 @@ def test_parquet_write_with_partitioning_readback_values(tmp_path):
     assert_df_equals(df.to_pandas(), read_back_pd_df)
 
     assert len(output_files) == 5
+    assert output_files._preview.preview_partition is None
+    output_files.__repr__()
     assert len(output_files._preview.preview_partition) == 5
 
 
@@ -193,6 +203,8 @@ def test_csv_write(tmp_path):
     assert_df_equals(df.to_pandas(), read_back_pd_df)
 
     assert len(pd_df) == 1
+    assert pd_df._preview.preview_partition is None
+    pd_df.__repr__()
     assert len(pd_df._preview.preview_partition) == 1
 
 

tests/dataframe/test_decimals.py

@@ -24,7 +24,7 @@ def test_decimal_parquet_roundtrip() -> None:
         df.write_parquet(dirname)
         df_readback = daft.read_parquet(dirname).collect()
 
-    assert str(df.to_pydict()["decimal128"]) == str(df_readback.to_pydict()["decimal128"])
+        assert str(df.to_pydict()["decimal128"]) == str(df_readback.to_pydict()["decimal128"])
 
 
 def test_arrow_decimal() -> None:

tests/dataframe/test_repr.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 import pandas as pd
+import pytest
 from PIL import Image
 
 import daft
@@ -86,6 +87,16 @@ def test_empty_repr(make_df):
     assert df._repr_html_() == "<small>(No data to display: Dataframe has no columns)</small>"
 
 
+@pytest.mark.parametrize("num_preview_rows", [9, 10, None])
+def test_repr_with_non_default_preview_rows(make_df, num_preview_rows):
+    df = make_df({"A": [i for i in range(10)], "B": [i for i in range(10)]})
+    df.collect(num_preview_rows=num_preview_rows)
+    df.__repr__()
+
+    assert df._preview.dataframe_num_rows == 10
+    assert len(df._preview.preview_partition) == (num_preview_rows if num_preview_rows is not None else 10)
+
+
 def test_empty_df_repr(make_df):
     df = make_df({"A": [1, 2, 3], "B": ["a", "b", "c"]})
     df = df.where(df["A"] > 10)

tests/dataframe/test_show.py

@@ -26,44 +26,47 @@ def test_show_some(make_df, valid_data, data_source):
     assert df_display.num_rows == 1
 
 
-def test_show_from_cached_collect(make_df, valid_data):
+def test_show_from_cached_repr(make_df, valid_data):
     df = make_df(valid_data)
     df = df.collect()
+    df.__repr__()
     collected_preview = df._preview
     df_display = df._construct_show_display(8)
 
-    # Check that cached preview from df.collect() was used.
+    # Check that cached preview from df.__repr__() was used.
     assert df_display.preview is collected_preview
     assert df_display.schema == df.schema()
     assert len(df_display.preview.preview_partition) == len(valid_data)
     assert df_display.preview.dataframe_num_rows == 3
     assert df_display.num_rows == 3
 
 
-def test_show_from_cached_collect_prefix(make_df, valid_data):
+def test_show_from_cached_repr_prefix(make_df, valid_data):
     df = make_df(valid_data)
     df = df.collect(3)
+    df.__repr__()
     df_display = df._construct_show_display(2)
 
     assert df_display.schema == df.schema()
     assert len(df_display.preview.preview_partition) == 2
-    # Check that a prefix of the cached preview from df.collect() was used, so dataframe_num_rows should be set.
+    # Check that a prefix of the cached preview from df.__repr__() was used, so dataframe_num_rows should be set.
     assert df_display.preview.dataframe_num_rows == 3
     assert df_display.num_rows == 2
 
 
-def test_show_not_from_cached_collect(make_df, valid_data, data_source):
+def test_show_not_from_cached_repr(make_df, valid_data, data_source):
     df = make_df(valid_data)
     df = df.collect(2)
+    df.__repr__()
     collected_preview = df._preview
     df_display = df._construct_show_display(8)
 
     variant = data_source
     if variant == "parquet":
-        # Cached preview from df.collect() is NOT USED because data was not materialized from parquet.
+        # Cached preview from df.__repr__() is NOT USED because data was not materialized from parquet.
         assert df_display.preview != collected_preview
     elif variant == "arrow":
-        # Cached preview from df.collect() is USED because data was materialized from arrow.
+        # Cached preview from df.__repr__() is USED because data was materialized from arrow.
         assert df_display.preview == collected_preview
     assert df_display.schema == df.schema()
     assert len(df_display.preview.preview_partition) == len(valid_data)

tests/dataframe/test_temporals.py

@@ -90,7 +90,7 @@ def test_temporal_file_roundtrip(format, use_native_downloader) -> None:
             df.write_parquet(dirname)
             df_readback = daft.read_parquet(dirname, use_native_downloader=use_native_downloader).collect()
 
-    assert df.to_pydict() == df_readback.to_pydict()
+        assert df.to_pydict() == df_readback.to_pydict()
 
 
 @pytest.mark.parametrize(