Merge remote-tracking branch 'upstream/main' into improve-import-err-msg

Author: KevsterAmp

README.md

@@ -5,7 +5,7 @@
 
 -----------------
 
-# pandas: powerful Python data analysis toolkit
+# pandas: A Powerful Python Data Analysis Toolkit
 
 | | |
 | --- | --- |

doc/source/getting_started/install.rst

@@ -308,7 +308,7 @@ Dependency                                             Minimum Version    pip ex
 `zlib <https://github.com/madler/zlib>`__                                 hdf5             Compression for HDF5
 `fastparquet <https://github.com/dask/fastparquet>`__  2024.2.0           -                Parquet reading / writing (pyarrow is default)
 `pyarrow <https://github.com/apache/arrow>`__          10.0.1             parquet, feather Parquet, ORC, and feather reading / writing
-`PyIceberg <https://py.iceberg.apache.org/>`__         0.7.1              iceberg          Apache Iceberg reading
+`PyIceberg <https://py.iceberg.apache.org/>`__         0.7.1              iceberg          Apache Iceberg reading / writing
 `pyreadstat <https://github.com/Roche/pyreadstat>`__   1.2.6              spss             SPSS files (.sav) reading
 `odfpy <https://github.com/eea/odfpy>`__               1.4.1              excel            Open document format (.odf, .ods, .odt) reading / writing
 ====================================================== ================== ================ ==========================================================

doc/source/reference/io.rst

@@ -162,6 +162,7 @@ Iceberg
     :toctree: api/
 
     read_iceberg
+    DataFrame.to_iceberg
 
 .. warning:: ``read_iceberg`` is experimental and may change without warning.
 

doc/source/user_guide/io.rst

@@ -29,7 +29,7 @@ The pandas I/O API is a set of top level ``reader`` functions accessed like
     binary,`HDF5 Format <https://support.hdfgroup.org/documentation/hdf5/latest/_intro_h_d_f5.html>`__, :ref:`read_hdf<io.hdf5>`, :ref:`to_hdf<io.hdf5>`
     binary,`Feather Format <https://github.com/wesm/feather>`__, :ref:`read_feather<io.feather>`, :ref:`to_feather<io.feather>`
     binary,`Parquet Format <https://parquet.apache.org/>`__, :ref:`read_parquet<io.parquet>`, :ref:`to_parquet<io.parquet>`
-    binary,`Apache Iceberg <https://iceberg.apache.org/>`__, :ref:`read_iceberg<io.iceberg>` , NA
+    binary,`Apache Iceberg <https://iceberg.apache.org/>`__, :ref:`read_iceberg<io.iceberg>` , :ref:`to_iceberg<io.iceberg>`
     binary,`ORC Format <https://orc.apache.org/>`__, :ref:`read_orc<io.orc>`, :ref:`to_orc<io.orc>`
     binary,`Stata <https://en.wikipedia.org/wiki/Stata>`__, :ref:`read_stata<io.stata_reader>`, :ref:`to_stata<io.stata_writer>`
     binary,`SAS <https://en.wikipedia.org/wiki/SAS_(software)>`__, :ref:`read_sas<io.sas_reader>` , NA
@@ -5417,7 +5417,7 @@ engines to safely work with the same tables at the same time.
 
 Iceberg support predicate pushdown and column pruning, which are available to pandas
 users via the ``row_filter`` and ``selected_fields`` parameters of the :func:`~pandas.read_iceberg`
-function. This is convenient to extract from large tables a subset that fits in memory asa
+function. This is convenient to extract from large tables a subset that fits in memory as a
 pandas ``DataFrame``.
 
 Internally, pandas uses PyIceberg_ to query Iceberg.
@@ -5497,6 +5497,29 @@ parameter:
 Reading a particular snapshot is also possible providing the snapshot ID as an argument to
 ``snapshot_id``.
 
+To save a ``DataFrame`` to Iceberg, it can be done with the :meth:`DataFrame.to_iceberg`
+method:
+
+.. code-block:: python
+
+    df.to_iceberg("my_table", catalog_name="my_catalog")
+
+To specify the catalog, it works in the same way as for :func:`read_iceberg` with the
+``catalog_name`` and ``catalog_properties`` parameters.
+
+The location of the table can be specified with the ``location`` parameter:
+
+.. code-block:: python
+
+    df.to_iceberg(
+        "my_table",
+        catalog_name="my_catalog",
+        location="s://my-data-lake/my-iceberg-tables",
+    )
+
+It is possible to add properties to the table snapshot by passing a dictionary to the
+``snapshot_properties`` parameter.
+
 More information about the Iceberg format can be found in the `Apache Iceberg official
 page <https://iceberg.apache.org/>`__.
 

doc/source/whatsnew/v3.0.0.rst

@@ -79,7 +79,7 @@ Other enhancements
 - :py:class:`frozenset` elements in pandas objects are now natively printed (:issue:`60690`)
 - Add ``"delete_rows"`` option to ``if_exists`` argument in :meth:`DataFrame.to_sql` deleting all records of the table before inserting data (:issue:`37210`).
 - Added half-year offset classes :class:`HalfYearBegin`, :class:`HalfYearEnd`, :class:`BHalfYearBegin` and :class:`BHalfYearEnd` (:issue:`60928`)
-- Added support to read from Apache Iceberg tables with the new :func:`read_iceberg` function (:issue:`61383`)
+- Added support to read and write from and to Apache Iceberg tables with the new :func:`read_iceberg` and :meth:`DataFrame.to_iceberg` functions (:issue:`61383`)
 - Errors occurring during SQL I/O will now throw a generic :class:`.DatabaseError` instead of the raw Exception type from the underlying driver manager library (:issue:`60748`)
 - Implemented :meth:`Series.str.isascii` and :meth:`Series.str.isascii` (:issue:`59091`)
 - Improved deprecation message for offset aliases (:issue:`60820`)
@@ -712,6 +712,7 @@ Timezones
 Numeric
 ^^^^^^^
 - Bug in :meth:`DataFrame.corr` where numerical precision errors resulted in correlations above ``1.0`` (:issue:`61120`)
+- Bug in :meth:`DataFrame.cov` raises a ``TypeError`` instead of returning potentially incorrect results or other errors (:issue:`53115`)
 - Bug in :meth:`DataFrame.quantile` where the column type was not preserved when ``numeric_only=True`` with a list-like ``q`` produced an empty result (:issue:`59035`)
 - Bug in :meth:`Series.dot` returning ``object`` dtype for :class:`ArrowDtype` and nullable-dtype data (:issue:`61375`)
 - Bug in ``np.matmul`` with :class:`Index` inputs raising a ``TypeError`` (:issue:`57079`)

pandas/core/frame.py

@@ -3547,6 +3547,62 @@ def to_xml(
 
         return xml_formatter.write_output()
 
+    def to_iceberg(
+        self,
+        table_identifier: str,
+        catalog_name: str | None = None,
+        *,
+        catalog_properties: dict[str, Any] | None = None,
+        location: str | None = None,
+        append: bool = False,
+        snapshot_properties: dict[str, str] | None = None,
+    ) -> None:
+        """
+        Write a DataFrame to an Apache Iceberg table.
+
+        .. versionadded:: 3.0.0
+
+        .. warning::
+
+           to_iceberg is experimental and may change without warning.
+
+        Parameters
+        ----------
+        table_identifier : str
+            Table identifier.
+        catalog_name : str, optional
+            The name of the catalog.
+        catalog_properties : dict of {str: str}, optional
+            The properties that are used next to the catalog configuration.
+        location : str, optional
+            Location for the table.
+        append : bool, default False
+            If ``True``, append data to the table, instead of replacing the content.
+        snapshot_properties : dict of {str: str}, optional
+            Custom properties to be added to the snapshot summary
+
+        See Also
+        --------
+        read_iceberg : Read an Apache Iceberg table.
+        DataFrame.to_parquet : Write a DataFrame in Parquet format.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={"col1": [1, 2], "col2": [4, 3]})
+        >>> df.to_iceberg("my_table", catalog_name="my_catalog")  # doctest: +SKIP
+        """
+        from pandas.io.iceberg import to_iceberg
+
+        to_iceberg(
+            self,
+            table_identifier,
+            catalog_name,
+            catalog_properties=catalog_properties,
+            location=location,
+            append=append,
+            snapshot_properties=snapshot_properties,
+        )
+
     # ----------------------------------------------------------------------
     @doc(INFO_DOCSTRING, **frame_sub_kwargs)
     def info(

pandas/io/iceberg.py

@@ -10,6 +10,7 @@
 def read_iceberg(
     table_identifier: str,
     catalog_name: str | None = None,
+    *,
     catalog_properties: dict[str, Any] | None = None,
     row_filter: str | None = None,
     selected_fields: tuple[str] | None = None,
@@ -21,6 +22,8 @@ def read_iceberg(
     """
     Read an Apache Iceberg table into a pandas DataFrame.
 
+    .. versionadded:: 3.0.0
+
     .. warning::
 
        read_iceberg is experimental and may change without warning.
@@ -71,7 +74,6 @@ def read_iceberg(
     """
     pyiceberg_catalog = import_optional_dependency("pyiceberg.catalog")
     pyiceberg_expressions = import_optional_dependency("pyiceberg.expressions")
-
     if catalog_properties is None:
         catalog_properties = {}
     catalog = pyiceberg_catalog.load_catalog(catalog_name, **catalog_properties)
@@ -91,3 +93,59 @@ def read_iceberg(
         limit=limit,
     )
     return result.to_pandas()
+
+
+def to_iceberg(
+    df: DataFrame,
+    table_identifier: str,
+    catalog_name: str | None = None,
+    *,
+    catalog_properties: dict[str, Any] | None = None,
+    location: str | None = None,
+    append: bool = False,
+    snapshot_properties: dict[str, str] | None = None,
+) -> None:
+    """
+    Write a DataFrame to an Apache Iceberg table.
+
+    .. versionadded:: 3.0.0
+
+    Parameters
+    ----------
+    table_identifier : str
+        Table identifier.
+    catalog_name : str, optional
+        The name of the catalog.
+    catalog_properties : dict of {str: str}, optional
+        The properties that are used next to the catalog configuration.
+    location : str, optional
+        Location for the table.
+    append : bool, default False
+        If ``True``, append data to the table, instead of replacing the content.
+    snapshot_properties : dict of {str: str}, optional
+        Custom properties to be added to the snapshot summary
+
+    See Also
+    --------
+    read_iceberg : Read an Apache Iceberg table.
+    DataFrame.to_parquet : Write a DataFrame in Parquet format.
+    """
+    pa = import_optional_dependency("pyarrow")
+    pyiceberg_catalog = import_optional_dependency("pyiceberg.catalog")
+    if catalog_properties is None:
+        catalog_properties = {}
+    catalog = pyiceberg_catalog.load_catalog(catalog_name, **catalog_properties)
+    arrow_table = pa.Table.from_pandas(df)
+    table = catalog.create_table_if_not_exists(
+        identifier=table_identifier,
+        schema=arrow_table.schema,
+        location=location,
+        # we could add `partition_spec`, `sort_order` and `properties` in the
+        # future, but it may not be trivial without exposing PyIceberg objects
+    )
+    if snapshot_properties is None:
+        snapshot_properties = {}
+    if append:
+        table.append(arrow_table, snapshot_properties=snapshot_properties)
+    else:
+        table.overwrite(arrow_table, snapshot_properties=snapshot_properties)

pandas/tests/io/test_iceberg.py

@@ -22,7 +22,7 @@
 pyiceberg_catalog = pytest.importorskip("pyiceberg.catalog")
 pq = pytest.importorskip("pyarrow.parquet")
 
-Catalog = collections.namedtuple("Catalog", ["name", "uri"])
+Catalog = collections.namedtuple("Catalog", ["name", "uri", "warehouse"])
 
 
 @pytest.fixture
@@ -58,7 +58,7 @@ def catalog(request, tmp_path):
 
         importlib.reload(pyiceberg_catalog)  # needed to reload the config file
 
-    yield Catalog(name=catalog_name or "default", uri=uri)
+    yield Catalog(name=catalog_name or "default", uri=uri, warehouse=warehouse)
 
     if catalog_name is not None:
         config_path.unlink()
@@ -141,3 +141,82 @@ def test_read_with_limit(self, catalog):
             limit=2,
         )
         tm.assert_frame_equal(result, expected)
+
+    def test_write(self, catalog):
+        df = pd.DataFrame(
+            {
+                "A": [1, 2, 3],
+                "B": ["foo", "foo", "foo"],
+            }
+        )
+        df.to_iceberg(
+            "ns.new_table",
+            catalog_properties={"uri": catalog.uri},
+            location=catalog.warehouse,
+        )
+        result = read_iceberg(
+            "ns.new_table",
+            catalog_properties={"uri": catalog.uri},
+        )
+        tm.assert_frame_equal(result, df)
+
+    @pytest.mark.parametrize("catalog", ["default", "pandas_tests"], indirect=True)
+    def test_write_by_catalog_name(self, catalog):
+        df = pd.DataFrame(
+            {
+                "A": [1, 2, 3],
+                "B": ["foo", "foo", "foo"],
+            }
+        )
+        df.to_iceberg(
+            "ns.new_table",
+            catalog_name=catalog.name,
+        )
+        result = read_iceberg(
+            "ns.new_table",
+            catalog_name=catalog.name,
+        )
+        tm.assert_frame_equal(result, df)
+
+    def test_write_existing_table_with_append_true(self, catalog):
+        original = read_iceberg(
+            "ns.my_table",
+            catalog_properties={"uri": catalog.uri},
+        )
+        new = pd.DataFrame(
+            {
+                "A": [1, 2, 3],
+                "B": ["foo", "foo", "foo"],
+            }
+        )
+        expected = pd.concat([original, new], ignore_index=True)
+        new.to_iceberg(
+            "ns.my_table",
+            catalog_properties={"uri": catalog.uri},
+            location=catalog.warehouse,
+            append=True,
+        )
+        result = read_iceberg(
+            "ns.my_table",
+            catalog_properties={"uri": catalog.uri},
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_write_existing_table_with_append_false(self, catalog):
+        df = pd.DataFrame(
+            {
+                "A": [1, 2, 3],
+                "B": ["foo", "foo", "foo"],
+            }
+        )
+        df.to_iceberg(
+            "ns.my_table",
+            catalog_properties={"uri": catalog.uri},
+            location=catalog.warehouse,
+            append=False,
+        )
+        result = read_iceberg(
+            "ns.my_table",
+            catalog_properties={"uri": catalog.uri},
+        )
+        tm.assert_frame_equal(result, df)

web/pandas/about/roadmap.md

@@ -58,27 +58,6 @@ library, making their behavior more consistent with the handling of
 NumPy arrays. We'll do this by cleaning up pandas' internals and
 adding new methods to the extension array interface.
 
-### String data type
-
-Currently, pandas stores text data in an `object` -dtype NumPy array.
-The current implementation has two primary drawbacks: First, `object`
--dtype is not specific to strings: any Python object can be stored in an
-`object` -dtype array, not just strings. Second: this is not efficient.
-The NumPy memory model isn't especially well-suited to variable width
-text data.
-
-To solve the first issue, we propose a new extension type for string
-data. This will initially be opt-in, with users explicitly requesting
-`dtype="string"`. The array backing this string dtype may initially be
-the current implementation: an `object` -dtype NumPy array of Python
-strings.
-
-To solve the second issue (performance), we'll explore alternative
-in-memory array libraries (for example, Apache Arrow). As part of the
-work, we may need to implement certain operations expected by pandas
-users (for example the algorithm used in, `Series.str.upper`). That work
-may be done outside of pandas.
-
 ### Apache Arrow interoperability
 
 [Apache Arrow](https://arrow.apache.org) is a cross-language development