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pandas.DataFrame.to_hdf

DataFrame.to_hdf(path_or_buf, key, **kwargs)[source]

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.

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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