@@ -5417,7 +5417,7 @@ engines to safely work with the same tables at the same time.
54175417
54185418Iceberg support predicate pushdown and column pruning, which are available to pandas
54195419users via the ``row_filter `` and ``selected_fields `` parameters of the :func: `~pandas.read_iceberg `
5420- function. This is convenient to extract from large tables a subset that fits in memory asa
5420+ function. This is convenient to extract from large tables a subset that fits in memory as a
54215421pandas ``DataFrame ``.
54225422
54235423Internally, pandas uses PyIceberg _ to query Iceberg.
@@ -5497,6 +5497,29 @@ parameter:
54975497 Reading a particular snapshot is also possible providing the snapshot ID as an argument to
54985498``snapshot_id ``.
54995499
5500+ To save a ``DataFrame `` to Iceberg, it can be done with the :meth: `DataFrame.to_iceberg `
5501+ method:
5502+
5503+ .. code-block :: python
5504+
5505+ df.to_iceberg(" my_table" , catalog_name = " my_catalog" )
5506+
5507+ To specify the catalog, it works in the same way as for :func: `read_iceberg ` with the
5508+ ``catalog_name `` and ``catalog_properties `` parameters.
5509+
5510+ The location of the table can be specified with the ``location `` parameter:
5511+
5512+ .. code-block :: python
5513+
5514+ df.to_iceberg(
5515+ " my_table" ,
5516+ catalog_name = " my_catalog" ,
5517+ location = " s://my-data-lake/my-iceberg-tables" ,
5518+ )
5519+
5520+ It is possible to add properties to the table snapshot by passing a dictionary to the
5521+ ``snapshot_properties `` parameter.
5522+
55005523More information about the Iceberg format can be found in the `Apache Iceberg official
55015524page <https://iceberg.apache.org/> `__.
55025525
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