to_hdf(self, path_or_buf, key: 'str', mode: 'str' = 'a', complevel: 'int | None' = None, complib: 'str | None' = None, append: 'bool_t' = False, format: 'str | None' = None, index: 'bool_t' = True, min_itemsize: 'int | dict[str, int] | None' = None, nan_rep=None, dropna: 'bool_t | None' = None, data_columns: 'Literal[True] | list[str] | None' = None, errors: 'str' = 'strict', encoding: 'str' = 'UTF-8') -> 'None'
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.
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 <io.hdf5>
.
File path or HDFStore object.
Identifier for the group in the store.
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.
Specifies a compression level for data. A value of 0 or None disables compression.
Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'bloscblosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError.
For Table formats, append the input data to the existing.
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".
Specifies how encoding and decoding errors are to be handled. See the errors argument for open
for a full list of options.
Map column names to minimum string sizes for columns.
How to represent null values as str. Not allowed with append=True.
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 io.hdf5-query-data-columns
. Applicable only to format='table'.
Write the contained data to an HDF5 file using HDFStore.
DataFrame.to_csv
Write out to a csv file.
DataFrame.to_feather
Write out feather-format for DataFrames.
DataFrame.to_parquet
Write a DataFrame to the binary parquet format.
DataFrame.to_sql
Write to a SQL table.
read_hdf
Read from HDF file.
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
... index=['a', 'b', 'c']) # doctest: +SKIP
... df.to_hdf('data.h5', key='df', mode='w') # doctest: +SKIP
We can add another object to the same file:
This example is valid syntax, but we were not able to check execution>>> s = pd.Series([1, 2, 3, 4]) # doctest: +SKIP
... s.to_hdf('data.h5', key='s') # doctest: +SKIP
Reading from HDF file:
This example is valid syntax, but we were not able to check execution>>> pd.read_hdf('data.h5', 'df') # doctest: +SKIP A B a 1 4 b 2 5 c 3 6This example is valid syntax, but we were not able to check execution
>>> pd.read_hdf('data.h5', 's') # doctest: +SKIP 0 1 1 2 2 3 3 4 dtype: int64See :
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