labels not in given in level (GH8594) • 1.9.12 I/O • read_csv() now accepts binary mode file buffers when using the Python csv engine (GH23779) • Bug in DataFrame.to_json() where using a Tuple as a 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 Using if/truth statements with pandas. NumPy ufuncs pandas.NA implements NumPy’s __array_ufunc__ protocol. Most ufuncs work with NA, and generally return NA: In [168]: np.log(pd.NA) Out[168]: In

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

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

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 Using if/truth statements with pandas. NumPy ufuncs pandas.NA implements NumPy’s __array_ufunc__ protocol. Most ufuncs work with NA, and generally return NA: In [168]: np.log(pd.NA) Out[168]: In handler (e.g. via builtin open function) or StringIO. 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

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 Using if/truth statements with pandas. NumPy ufuncs pandas.NA implements NumPy’s __array_ufunc__ protocol. Most ufuncs work with NA, and generally return NA: In [168]: np.log(pd.NA) Out[168]: In handler (e.g. via builtin open function) or StringIO. 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