Series.to_pickle() and DataFrame.to_pickle() have gained a protocol parameter (GH16252). By default, this parameter is set to HIGHEST_PROTOCOL • read_feather() has gained the nthreads parameter for multi-threaded import time of pandas by about 2.25x. (GH16764) • Support for PEP 519 – Adding a file system path protocol on most readers (e.g. read_csv()) and writers (e.g. DataFrame.to_csv()) (GH13823). • Added a __fspath__ method to pd.HDFStore, pd.ExcelFile, and pd.ExcelWriter to work prop- erly with the file system path protocol (GH13823). • The validate argument for merge() now checks whether a merge is one-to-one, one-to-many

suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the __fspath__ protocol. This includes pathlib.Path and py._path.local.LocalPath objects. New in version 0.19.0: support for pathlib, py.path. New in version 0.21.0: support for __fspath__ protocol. key [object PeriodIndex with desired frequency (inferred from index if not passed). to_pickle(path[, compression, protocol]) Pickle (serialize) object to file. to_sparse([kind, fill_value]) Convert Series to SparseSeries

also be a dict in order to pass options to the compression protocol. It must have a 'method' key set to the name of the compression protocol, which must be one of {'zip', 'gzip', 'bz2', 'xz', 'zstd'}. 533097 999 -0.140850 Name: A, Length: 1000, dtype: float64 Passing options to the compression protocol in order to speed up compression: In [435]: df.to_pickle("data.pkl.gz", compression={"method": suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the

also be a dict in order to pass options to the compression protocol. It must have a 'method' key set to the name of the compression protocol, which must be one of {'zip', 'gzip', 'bz2'}. All other key-value 361779 999 -1.197988 Name: A, Length: 1000, dtype: float64 Passing options to the compression protocol in order to speed up compression: In [345]: df.to_pickle( .....: "data.pkl.gz", .....: compression={"method": suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the

also be a dict in order to pass options to the compression protocol. It must have a 'method' key set to the name of the compression protocol, which must be one of {'zip', 'gzip', 'bz2'}. All other key-value 361779 999 -1.197988 Name: A, Length: 1000, dtype: float64 Passing options to the compression protocol in order to speed up compression: In [345]: df.to_pickle( .....: "data.pkl.gz", .....: compression={"method": suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the

also be a dict in order to pass options to the compression protocol. It must have a 'method' key set to the name of the compression protocol, which must be one of {'zip', 'gzip', 'bz2'}. All other key-value 361779 999 -1.197988 Name: A, Length: 1000, dtype: float64 Passing options to the compression protocol in order to speed up compression: In [344]: df.to_pickle("data.pkl.gz", compression={"method": suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the

also be a dict in order to pass options to the compression protocol. It must have a 'method' key set to the name of the compression protocol, which must be one of {'zip', 'gzip', 'bz2'}. All other key-value 361779 999 -1.197988 Name: A, Length: 1000, dtype: float64 Passing options to the compression protocol in order to speed up compression: In [393]: df.to_pickle("data.pkl.gz", compression={"method": suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the

also be a dict in order to pass options to the compression protocol. It must have a 'method' key set to the name of the compression protocol, which must be one of {'zip', 'gzip', 'bz2'}. All other key-value 361779 999 -1.197988 Name: A, Length: 1000, dtype: float64 Passing options to the compression protocol in order to speed up compression: In [393]: df.to_pickle("data.pkl.gz", compression={"method": suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the

also be a dict in order to pass options to the compression protocol. It must have a 'method' key set to the name of the compression protocol, which must be one of {'zip', 'gzip', 'bz2'}. All other key-value 361779 999 -1.197988 Name: A, Length: 1000, dtype: float64 Passing options to the compression protocol in order to speed up compression: In [393]: df.to_pickle("data.pkl.gz", compression={"method": suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the

suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the in version 0.19.0: support for pathlib, py.path. New in version 0.21.0: support for __fspath__ protocol. key [object, optional] The group identifier in the store. Can be omitted if the HDF file contains with desired frequency (inferred from index if not passed). to_pickle(self, path[, compression, protocol]) Pickle (serialize) object to file. to_sparse(self[, kind, fill_value]) (DEPRECATED) Convert Series