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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:
    key str

      Key of object to append.

    value{Series, DataFrame}

      Value of object to append.

    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.

    axes default None

      This parameter is currently not accepted.

    index bool, default True

      Write DataFrame index as a column.

    append bool, default True

      Append the input data to the existing.

    complib default None

      This parameter is currently not accepted.

    complevel int, 0-9, default None

      Specifies a compression level for data. A value of 0 or None disables compression.

    columns default None

      This parameter is currently not accepted, try data_columns.

    min_itemsize int, dict, or None

      Dict of columns that specify minimum str sizes.

    nan_rep str

      Str to use as str nan representation.

    chunksize int or None

      Size to chunk the writing.

    expectedrows int

      Expected TOTAL row size of this table.

    dropna bool, default False, optional

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

    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.

    encoding default None

      Provide an encoding for str.

    errors str, default ‘strict’

      The error handling scheme to use for encoding errors. The default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors.

  See also

  `HDFStore.append_to_multiple`

    Append to multiple tables.

  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