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# pandas.HDFStore.put

HDFStore.put(key, value, format=None, index=True, append=False, complib=None, complevel=None, min_itemsize=None, nan_rep=None, data_columns=None, encoding=None, errors='strict', track_times=True, dropna=False)

  Store object in HDFStore.

  Parameters:
    key str
    value{Series, DataFrame}
    format‘fixed(f)|table(t)’, default is ‘fixed’

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

      `'fixed'`

        Fixed format. Fast writing/reading. Not-appendable, nor searchable.

      `'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.

    index bool, default True

      Write DataFrame index as a column.

    append bool, default False

      This will force Table format, append the input data to the existing.

    data_columns list of columns or True, default None

      List of columns to create as data columns, or True to use all columns. See here.

    encoding str, default None

      Provide an encoding for strings.

    track_times bool, default True

      Parameter is propagated to ‘create_table’ method of ‘PyTables’. If set to False it enables to have the same h5 files (same hashes) independent on creation time.

    dropna bool, default False, optional

      Remove missing values.

  Examples

      >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
      >>> store = pd.HDFStore("store.h5", 'w')
      >>> store.put('data', df)