pandas.Series.to_hdf

Series.to_hdf(self, 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. 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')