@@ -5417,7 +5417,7 @@ engines to safely work with the same tables at the same time.
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Iceberg support predicate pushdown and column pruning, which are available to pandas
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users via the ``row_filter `` and ``selected_fields `` parameters of the :func: `~pandas.read_iceberg `
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- function. This is convenient to extract from large tables a subset that fits in memory asa
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+ function. This is convenient to extract from large tables a subset that fits in memory as a
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pandas ``DataFrame ``.
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Internally, pandas uses PyIceberg _ to query Iceberg.
@@ -5497,6 +5497,29 @@ parameter:
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Reading a particular snapshot is also possible providing the snapshot ID as an argument to
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``snapshot_id ``.
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+ To save a ``DataFrame `` to Iceberg, it can be done with the :meth: `DataFrame.to_iceberg `
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+ method:
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+
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+ .. code-block :: python
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+
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+ df.to_iceberg(" my_table" , catalog_name = " my_catalog" )
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+
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+ To specify the catalog, it works in the same way as for :func: `read_iceberg ` with the
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+ ``catalog_name `` and ``catalog_properties `` parameters.
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+
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+ The location of the table can be specified with the ``location `` parameter:
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+
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+ .. code-block :: python
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+
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+ df.to_iceberg(
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+ " my_table" ,
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+ catalog_name = " my_catalog" ,
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+ location = " s://my-data-lake/my-iceberg-tables" ,
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+ )
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+
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+ It is possible to add properties to the table snapshot by passing a dictionary to the
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+ ``snapshot_properties `` parameter.
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+
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More information about the Iceberg format can be found in the `Apache Iceberg official
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page <https://iceberg.apache.org/> `__.
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