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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:
    keystr
    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.

    indexbool, default True

      Write DataFrame index as a column.

    appendbool, default False

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

    data_columnslist of columns or True, default None

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

    encodingstr, default None

      Provide an encoding for strings.

    track_timesbool, 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.

    dropnabool, 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)