# pandas.HDFStore.append

HDFStore.append(key, value, format=None, axes=None, index=True, append=True, complib=None, complevel=None, columns=None, min_itemsize=None, nan_rep=None, chunksize=None, expectedrows=None, dropna=None, data_columns=None, encoding=None, errors='strict')

  Append to Table in file. Node must already exist and be Table format.

  Parameters
    key str
    value{Series, DataFrame}
    format‘table’ is the default

      Format to use when storing object in HDFStore. Value can be one of:

      `'table'`

        Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.

    append bool, default True

      Append the input data to the existing.

    data_columns list of columns, or True, default None

      List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See here.

    min_itemsize dict of columns that specify minimum str sizes
    nan_rep str to use as str nan representation
    chunksize size to chunk the writing
    expectedrows expected TOTAL row size of this table
    encoding default None, provide an encoding for str
    dropna bool, default False

      Do not write an ALL nan row to the store settable by the option ‘io.hdf.dropna_table’.

  Notes

  Does not check if data being appended overlaps with existing data in the table, so be careful