# pandas.Series.to_hdf

`Series.``to_hdf`(path_or_buf, key, **kwargs)

  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.

  For more information see the user guide.

  Parameters:

  path_or_buf : str or pandas.HDFStore

  > File path or HDFStore object.

  key : str

  > 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.

  format : {‘fixed’, ‘table’}, 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.

  append : bool, default False

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

  data_columns : list 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. Applicable only to format=’table’.

  complevel : {0-9}, optional

  > Specifies a compression level for data. A value of 0 disables compression.

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

  > Specifies the compression library to be used. As of v0.20.2 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.

  fletcher32 : bool, default False

  > If applying compression use the fletcher32 checksum.

  dropna : bool, default False

  > If true, ALL nan rows will not be written to store.

  errors : str, default ‘strict’

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

  See also

  `DataFrame.read_hdf`
    Read from HDF file.
  `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

  Deleting file with data:

      >>> import os
      >>> os.remove('data.h5')

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