Support for partition_cols in to_parquet

doc/source/whatsnew/v0.24.0.txt

@@ -213,6 +213,7 @@ Other Enhancements
 - New attribute :attr:`__git_version__` will return git commit sha of current build (:issue:`21295`).
 - Compatibility with Matplotlib 3.0 (:issue:`22790`).
 - Added :meth:`Interval.overlaps`, :meth:`IntervalArray.overlaps`, and :meth:`IntervalIndex.overlaps` for determining overlaps between interval-like objects (:issue:`21998`)
+- :func:`~DataFrame.to_parquet` now supports writing a DataFrame as a directory of parquet files partitioned by a subset of the columns. (:issue:`23283`).
 - :meth:`Timestamp.tz_localize`, :meth:`DatetimeIndex.tz_localize`, and :meth:`Series.tz_localize` have gained the ``nonexistent`` argument for alternative handling of nonexistent times. See :ref:`timeseries.timezone_nonexsistent` (:issue:`8917`)
 
 .. _whatsnew_0240.api_breaking:

pandas/io/parquet.py

@@ -125,9 +125,14 @@ def write(self, df, path, compression='snappy',
 
         else:
             table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
-            self.api.parquet.write_table(
-                table, path, compression=compression,
-                coerce_timestamps=coerce_timestamps, **kwargs)
+            if 'partition_cols' in kwargs:
+                self.api.parquet.write_to_dataset(
+                    table, path, compression=compression,
+                    coerce_timestamps=coerce_timestamps, **kwargs)
+            else:
+                self.api.parquet.write_table(
+                    table, path, compression=compression,
+                    coerce_timestamps=coerce_timestamps, **kwargs)
 
     def read(self, path, columns=None, **kwargs):
         path, _, _, should_close = get_filepath_or_buffer(path)
@@ -252,7 +257,8 @@ def to_parquet(df, path, engine='auto', compression='snappy', index=None,
     ----------
     df : DataFrame
     path : string
-        File path
+        File path ( Will be used as `root_path` if
+        `partition_cols` is provided as parameter for 'pyarrow' engine).
     engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
         Parquet library to use. If 'auto', then the option
         ``io.parquet.engine`` is used. The default ``io.parquet.engine``

pandas/tests/io/test_parquet.py

@@ -1,6 +1,8 @@
 """ test parquet compat """
 
 import pytest
+import tempfile
+import shutil
 import datetime
 from distutils.version import LooseVersion
 from warnings import catch_warnings
@@ -478,6 +480,25 @@ def test_s3_roundtrip(self, df_compat, s3_resource, pa):
         check_round_trip(df_compat, pa,
                          path='s3://pandas-test/pyarrow.parquet')
 
+    def test_partition_cols_supported(self, pa_ge_070, df_full):
+        partition_cols = ['bool', 'int']
+        df = df_full
+        path = tempfile.mkdtemp()
+        df.to_parquet(path, partition_cols=partition_cols,
+                      compression=None)
+        import pyarrow.parquet as pq
+        dataset = pq.ParquetDataset(path, validate_schema=False)
+        assert len(dataset.partitions.partition_names) == 2
+        assert dataset.partitions.partition_names == set(partition_cols)
+        shutil.rmtree(path)
+
+    def test_ignore_partition_cols_lt_070(self, pa_lt_070, df_full):
+        partition_cols = ['bool', 'int']
+        pa = pa_lt_070
+        df = df_full
+        check_round_trip(df, pa,
+                         write_kwargs={'partition_cols': partition_cols})
+
 
 class TestParquetFastParquet(Base):