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# pandas.DataFrame.to_hdf

DataFrame.to_hdf(path_or_buf, *, key, mode='a', complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors='strict', encoding='UTF-8')

  Write the contained data to an HDF5 file using HDFStore.

  Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.

  In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.

  Warning

  One can store a subclass of `DataFrame` or `Series` to HDF5, but the type of the subclass is lost upon storing.

  For more information see the user guide.

  Parameters:
    path_or_bufstr or pandas.HDFStore

      File path or HDFStore object.

    keystr

      Identifier for the group in the store.

    mode{‘a’, ‘w’, ‘r+’}, default ‘a’

      Mode to open file:

      - ‘w’: write, a new file is created (an existing file with the same name would be deleted).

      - ‘a’: append, an existing file is opened for reading and writing, and if the file does not exist it is created.

      - ‘r+’: similar to ‘a’, but the file must already exist.

    complevel{0-9}, default None

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

    complib{‘zlib’, ‘lzo’, ‘bzip2’, ‘blosc’}, default ‘zlib’

      Specifies the compression library to be used. These additional compressors for Blosc are supported (default if no compressor specified: ‘blosc:blosclz’): {‘blosc:blosclz’, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’}. Specifying a compression library which is not available issues a ValueError.

    appendbool, default False

      For Table formats, append the input data to the existing.

    format{‘fixed’, ‘table’, None}, default ‘fixed’

      Possible values:

      - ‘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.

      - If None, pd.get_option(‘io.hdf.default_format’) is checked, followed by fallback to “fixed”.

    indexbool, default True

      Write DataFrame index as a column.

    min_itemsizedict or int, optional

      Map column names to minimum string sizes for columns.

    nan_repAny, optional

      How to represent null values as str. Not allowed with append=True.

    dropnabool, default False, optional

      Remove missing values.

    data_columnslist of columns or True, optional

      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 Query via data columns. for more information. Applicable only to format=’table’.

    errorsstr, default ‘strict’

      Specifies how encoding and decoding errors are to be handled. See the errors argument for `open()` for a full list of options.

    encodingstr, default “UTF-8”

  See also

  `read_hdf`

    Read from HDF file.

  `DataFrame.to_orc`

    Write a DataFrame to the binary orc format.

  `DataFrame.to_parquet`

    Write a DataFrame to the binary parquet format.

  `DataFrame.to_sql`

    Write to a SQL table.

  `DataFrame.to_feather`

    Write out feather-format for DataFrames.

  `DataFrame.to_csv`

    Write out to a csv file.

  Examples

      >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
      ...                   index=['a', 'b', 'c'])
      >>> df.to_hdf('data.h5', key='df', mode='w')

  We can add another object to the same file:

      >>> s = pd.Series([1, 2, 3, 4])
      >>> s.to_hdf('data.h5', key='s')

  Reading from HDF file:

      >>> pd.read_hdf('data.h5', 'df')
      A  B
      a  1  4
      b  2  5
      c  3  6
      >>> pd.read_hdf('data.h5', 's')
      0    1
      1    2
      2    3
      3    4
      dtype: int64