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# 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:
    keystr
    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.

    indexbool, default True

      Write DataFrame index as a column.

    appendbool, default True

      Append the input data to the existing.

    data_columnslist 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_itemsizedict of columns that specify minimum str sizes
    nan_repstr to use as str nan representation
    chunksizesize to chunk the writing
    expectedrowsexpected TOTAL row size of this table
    encodingdefault None, provide an encoding for str
    dropnabool, default False, optional

      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

  Examples

      >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
      >>> store = pd.HDFStore("store.h5", 'w')
      >>> store.put('data', df1, format='table')
      >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
      >>> store.append('data', df2)
      >>> store.close()
         A  B
      0  1  2
      1  3  4
      0  5  6
      1  7  8