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# pandas.read_iceberg

pandas.read_iceberg(table_identifier, catalog_name=None, *, catalog_properties=None, columns=None, row_filter=None, case_sensitive=True, snapshot_id=None, limit=None, scan_properties=None)

  Read an Apache Iceberg table into a pandas DataFrame.

  Added in version 3.0.0.

  Warning

  read_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.

    columns list of str, optional

      A list of strings representing the column names to return in the output dataframe.

    row_filter str, optional

      A string that describes the desired rows.

    case_sensitive bool, default True

      If True column matching is case sensitive.

    snapshot_id int, optional

      Snapshot ID to time travel to. By default the table will be scanned as of the current snapshot ID.

    limit int, optional

      An integer representing the number of rows to return in the scan result. By default all matching rows will be fetched.

    scan_properties dict of {str: obj}, optional

      Additional Table properties as a dictionary of string key value pairs to use for this scan.

  Returns:
    DataFrame

      DataFrame based on the Iceberg table.

  See also

  `read_parquet`

    Read a Parquet file.

  Examples

      >>> df = pd.read_iceberg(
      ...     table_identifier="my_table",
      ...     catalog_name="my_catalog",
      ...     catalog_properties={"s3.secret-access-key": "my-secret"},
      ...     row_filter="trip_distance >= 10.0",
      ...     columns=["VendorID", "tpep_pickup_datetime"],
      ... )