Support for partition_cols in to_parquet

doc/source/io.rst

@@ -4668,6 +4668,33 @@ Passing ``index=True`` will *always* write the index, even if that's not the
 underlying engine's default behavior.
 
 
+Partitioning Parquet files
+''''''''''''''''''''''''''
+
+Parquet supports partitioning of data based on the values of one or more columns.
+
+.. ipython:: python
+
+    df = pd.DataFrame({'a': [0, 0, 1, 1], 'b': [0, 1, 0, 1]})
+    df.to_parquet(fname='test', engine='pyarrow', partition_cols=['a'], compression=None)
+
+The `fname` specifies the parent directory to which data will be saved.
+The `partition_cols` are the column names by which the dataset will be partitioned.
+Columns are partitioned in the order they are given. The partition splits are
+determined by the unique values in the partition columns.
+The above example creates a partitioned dataset that may look like:
+
+::
+
+    test/
+        a=0/
+           0bac803e32dc42ae83fddfd029cbdebc.parquet
+           ...
+        a=1/
+           e6ab24a4f45147b49b54a662f0c412a3.parquet
+           ...
+
+
 .. _io.sql:
 
 SQL Queries

doc/source/whatsnew/v0.24.0.txt

@@ -235,6 +235,7 @@ 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`)
+- With the pyarrow engine, :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/core/frame.py

@@ -1970,7 +1970,7 @@ def to_feather(self, fname):
         to_feather(self, fname)
 
     def to_parquet(self, fname, engine='auto', compression='snappy',
-                   index=None, **kwargs):
+                   index=None, partition_cols=None, **kwargs):
         """
         Write a DataFrame to the binary parquet format.
 
@@ -1984,7 +1984,8 @@ def to_parquet(self, fname, engine='auto', compression='snappy',
         Parameters
         ----------
         fname : str
-            String file path.
+            File path or Root Directory path. Will be used as Root Directory
+            path while writing a partitioned dataset.
         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``
@@ -1997,6 +1998,11 @@ def to_parquet(self, fname, engine='auto', compression='snappy',
             If ``False``, they will not be written to the file. If ``None``,
             the behavior depends on the chosen engine.
 
+            .. versionadded:: 0.24.0
+        partition_cols : list, optional, default None
+            Column names by which to partition the dataset
+            Columns are partitioned in the order they are given
+
             .. versionadded:: 0.24.0
 
         **kwargs
@@ -2027,7 +2033,8 @@ def to_parquet(self, fname, engine='auto', compression='snappy',
         """
         from pandas.io.parquet import to_parquet
         to_parquet(self, fname, engine,
-                   compression=compression, index=index, **kwargs)
+                   compression=compression, index=index,
+                   partition_cols=partition_cols, **kwargs)
 
     @Substitution(header='Write out the column names. If a list of strings '
                          'is given, it is assumed to be aliases for the '

pandas/io/parquet.py

@@ -101,19 +101,25 @@ def __init__(self):
         self.api = pyarrow
 
     def write(self, df, path, compression='snappy',
-              coerce_timestamps='ms', index=None, **kwargs):
+              coerce_timestamps='ms', index=None, partition_cols=None,
+              **kwargs):
         self.validate_dataframe(df)
         path, _, _, _ = get_filepath_or_buffer(path, mode='wb')
 
         if index is None:
             from_pandas_kwargs = {}
         else:
             from_pandas_kwargs = {'preserve_index': index}
-
         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 is not None:
+            self.api.parquet.write_to_dataset(
+                table, path, compression=compression,
+                coerce_timestamps=coerce_timestamps,
+                partition_cols=partition_cols, **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)
@@ -156,12 +162,24 @@ def __init__(self):
             )
         self.api = fastparquet
 
-    def write(self, df, path, compression='snappy', index=None, **kwargs):
+    def write(self, df, path, compression='snappy', index=None,
+              partition_cols=None, **kwargs):
         self.validate_dataframe(df)
         # thriftpy/protocol/compact.py:339:
         # DeprecationWarning: tostring() is deprecated.
         # Use tobytes() instead.
 
+        if 'partition_on' in kwargs:
+            if partition_cols is None:
+                partition_cols = kwargs.pop('partition_on')
+            else:
+                raise ValueError("Cannot use both partition_on and "
+                                 "partition_cols. Use partition_cols for "
+                                 "partitioning data")
+
+        if partition_cols is not None:
+            kwargs['file_scheme'] = 'hive'
+
         if is_s3_url(path):
             # path is s3:// so we need to open the s3file in 'wb' mode.
             # TODO: Support 'ab'
@@ -174,7 +192,8 @@ def write(self, df, path, compression='snappy', index=None, **kwargs):
 
         with catch_warnings(record=True):
             self.api.write(path, df, compression=compression,
-                           write_index=index, **kwargs)
+                           write_index=index, partition_on=partition_cols,
+                           **kwargs)
 
     def read(self, path, columns=None, **kwargs):
         if is_s3_url(path):
@@ -194,15 +213,15 @@ def read(self, path, columns=None, **kwargs):
 
 
 def to_parquet(df, path, engine='auto', compression='snappy', index=None,
-               **kwargs):
+               partition_cols=None, **kwargs):
     """
     Write a DataFrame to the parquet format.
 
     Parameters
     ----------
-    df : DataFrame
-    path : string
-        File path
+    path : str
+        File path or Root Directory path. Will be used as Root Directory path
+        while writing a partitioned dataset.
     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``
@@ -216,11 +235,17 @@ def to_parquet(df, path, engine='auto', compression='snappy', index=None,
         engine's default behavior will be used.
 
         .. versionadded 0.24.0
+    partition_cols : list, optional, default None
+        Column names by which to partition the dataset
+        Columns are partitioned in the order they are given
+
+        .. versionadded:: 0.24.0
     kwargs
         Additional keyword arguments passed to the engine
     """
     impl = get_engine(engine)
-    return impl.write(df, path, compression=compression, index=index, **kwargs)
+    return impl.write(df, path, compression=compression, index=index,
+                      partition_cols=partition_cols, **kwargs)
 
 
 def read_parquet(path, engine='auto', columns=None, **kwargs):

pandas/tests/io/test_parquet.py

@@ -1,4 +1,5 @@
 """ test parquet compat """
+import os
 
 import pytest
 import datetime
@@ -454,6 +455,18 @@ 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, df_full):
+        # GH #23283
+        partition_cols = ['bool', 'int']
+        df = df_full
+        with tm.ensure_clean_dir() as path:
+            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)
+
 
 class TestParquetFastParquet(Base):
 
@@ -519,3 +532,37 @@ def test_s3_roundtrip(self, df_compat, s3_resource, fp):
         # GH #19134
         check_round_trip(df_compat, fp,
                          path='s3://pandas-test/fastparquet.parquet')
+
+    def test_partition_cols_supported(self, fp, df_full):
+        # GH #23283
+        partition_cols = ['bool', 'int']
+        df = df_full
+        with tm.ensure_clean_dir() as path:
+            df.to_parquet(path, engine="fastparquet",
+                          partition_cols=partition_cols, compression=None)
+            assert os.path.exists(path)
+            import fastparquet
+            actual_partition_cols = fastparquet.ParquetFile(path, False).cats
+            assert len(actual_partition_cols) == 2
+
+    def test_partition_on_supported(self, fp, df_full):
+        # GH #23283
+        partition_cols = ['bool', 'int']
+        df = df_full
+        with tm.ensure_clean_dir() as path:
+            df.to_parquet(path, engine="fastparquet", compression=None,
+                          partition_on=partition_cols)
+            assert os.path.exists(path)
+            import fastparquet
+            actual_partition_cols = fastparquet.ParquetFile(path, False).cats
+            assert len(actual_partition_cols) == 2
+
+    def test_error_on_using_partition_cols_and_partition_on(self, fp, df_full):
+        # GH #23283
+        partition_cols = ['bool', 'int']
+        df = df_full
+        with pytest.raises(ValueError):
+            with tm.ensure_clean_dir() as path:
+                df.to_parquet(path, engine="fastparquet", compression=None,
+                              partition_on=partition_cols,
+                              partition_cols=partition_cols)

pandas/tests/util/test_testing.py

@@ -876,3 +876,10 @@ def test_datapath_missing(datapath, request):
     )
 
     assert result == expected
+
+
+def test_create_temp_directory():
+    with tm.ensure_clean_dir() as path:
+        assert os.path.exists(path)
+        assert os.path.isdir(path)
+    assert not os.path.exists(path)

pandas/util/testing.py

@@ -6,6 +6,7 @@
 import locale
 import os
 import re
+from shutil import rmtree
 import string
 import subprocess
 import sys
@@ -759,6 +760,25 @@ def ensure_clean(filename=None, return_filelike=False):
                 print("Exception on removing file: {error}".format(error=e))
 
 
+@contextmanager
+def ensure_clean_dir():
+    """
+    Get a temporary directory path and agrees to remove on close.
+
+    Yields
+    ------
+    Temporary directory path
+    """
+    directory_name = tempfile.mkdtemp(suffix='')
+    try:
+        yield directory_name
+    finally:
+        try:
+            rmtree(directory_name)
+        except Exception:
+            pass
+
+
 # -----------------------------------------------------------------------------
 # Comparators