Merge branch 'pandas-dev:main' into bug-unhashable-columns

Author: Andre-Andreati

README.md

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

ci/deps/actions-310-minimum_versions.yaml

@@ -18,7 +18,7 @@ dependencies:
   - pytest-xdist>=3.4.0
   - pytest-localserver>=0.8.1
   - pytest-qt>=4.4.0
-  - boto3
+  - boto3=1.37.3
 
   # required dependencies
   - python-dateutil=2.8.2

ci/deps/actions-310.yaml

@@ -16,7 +16,7 @@ dependencies:
   - pytest-xdist>=3.4.0
   - pytest-localserver>=0.8.1
   - pytest-qt>=4.4.0
-  - boto3
+  - boto3=1.37.3
 
   # required dependencies
   - python-dateutil

ci/deps/actions-311-downstream_compat.yaml

@@ -17,7 +17,7 @@ dependencies:
   - pytest-xdist>=3.4.0
   - pytest-localserver>=0.8.1
   - pytest-qt>=4.4.0
-  - boto3
+  - boto3=1.37.3
 
   # required dependencies
   - python-dateutil

ci/deps/actions-311.yaml

@@ -16,7 +16,7 @@ dependencies:
   - pytest-xdist>=3.4.0
   - pytest-localserver>=0.8.1
   - pytest-qt>=4.4.0
-  - boto3
+  - boto3=1.37.3
 
   # required dependencies
   - python-dateutil

ci/deps/actions-312.yaml

@@ -16,7 +16,7 @@ dependencies:
   - pytest-xdist>=3.4.0
   - pytest-localserver>=0.8.1
   - pytest-qt>=4.4.0
-  - boto3
+  - boto3=1.37.3
 
   # required dependencies
   - python-dateutil

ci/deps/actions-313.yaml

@@ -16,7 +16,7 @@ dependencies:
   - pytest-xdist>=3.4.0
   - pytest-localserver>=0.8.1
   - pytest-qt>=4.4.0
-  - boto3
+  - boto3=1.37.3
 
   # required dependencies
   - python-dateutil

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/>`__.