xorbits.pandas.Series.to_hdf#
- Series.to_hdf(path_or_buf: FilePath | HDFStore, key: str, mode: Literal['a', 'w', 'r+'] = 'a', complevel: int | None = None, complib: Literal['zlib', 'lzo', 'bzip2', 'blosc'] | None = None, append: bool_t = False, format: Literal['fixed', 'table'] | 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: OpenFileErrors = 'strict', encoding: str = 'UTF-8') None #
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
orSeries
to HDF5, but the type of the subclass is lost upon storing.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.
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.
append (bool, 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”.
index (bool, default True) – Write DataFrame index as a column.
min_itemsize (dict or int, optional) – Map column names to minimum string sizes for columns.
nan_rep (Any, optional) – How to represent null values as str. Not allowed with append=True.
dropna (bool, default False, optional) – Remove missing values.
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. for more information. Applicable only to format=’table’.
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.encoding (str, 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
Warning
This method has not been implemented yet. Xorbits will try to execute it with pandas.
This docstring was copied from pandas.core.series.Series.