def __init__(self,
fname,
data,
convert_dates=None,
encoding="latin-1",
byteorder=None):
warnings.warn(
"StataWriter is deprecated as of 0.10.0 and will be removed in a "
"future version. Use pandas.DataFrame.to_stata or "
"pandas.io.stata.StatWriter instead.", FutureWarning)
self._convert_dates = convert_dates
# attach nobs, nvars, data, varlist, typlist
if data_util._is_using_pandas(data, None):
self._prepare_pandas(data)
elif data_util._is_array_like(data, None):
data = np.asarray(data)
if data_util._is_structured_ndarray(data):
self._prepare_structured_array(data)
else:
if convert_dates is not None:
raise ValueError("Not able to convert dates in a plain"
" ndarray.")
self._prepare_ndarray(data)
else: # pragma : no cover
raise ValueError("Type %s for data not understood" % type(data))
if byteorder is None:
byteorder = sys.byteorder
self._byteorder = _set_endianness(byteorder)
self._encoding = encoding
self._file = get_file_obj(fname, 'wb', encoding)
def __init__(self, fname, data, convert_dates=None, encoding="latin-1",
byteorder=None):
self._convert_dates = convert_dates
# attach nobs, nvars, data, varlist, typlist
if data_util._is_using_pandas(data, None):
self._prepare_pandas(data)
elif data_util._is_array_like(data, None):
data = np.asarray(data)
if data_util._is_structured_ndarray(data):
self._prepare_structured_array(data)
else:
if convert_dates is not None:
raise ValueError("Not able to convert dates in a plain"
" ndarray.")
self._prepare_ndarray(data)
else: # pragma : no cover
raise ValueError("Type %s for data not understood" % type(data))
if byteorder is None:
byteorder = sys.byteorder
self._byteorder = _set_endianness(byteorder)
self._encoding = encoding
self._file = get_file_obj(fname, 'wb', encoding)
def __init__(self, fname, data, convert_dates=None, encoding="latin-1",
byteorder=None):
self._convert_dates = convert_dates
# attach nobs, nvars, data, varlist, typlist
if data_util._is_using_pandas(data, None):
self._prepare_pandas(data)
elif data_util._is_array_like(data, None):
data = np.asarray(data)
if data_util._is_structured_ndarray(data):
self._prepare_structured_array(data)
else:
if convert_dates is not None:
raise ValueError("Not able to convert dates in a plain"
" ndarray.")
self._prepare_ndarray(data)
else: # pragma : no cover
raise ValueError("Type %s for data not understood" % type(data))
if byteorder is None:
byteorder = sys.byteorder
self._byteorder = _set_endianness(byteorder)
self._encoding = encoding
self._file = get_file_obj(fname, 'wb', encoding)
def __init__(self, fname, data, convert_dates=None, encoding="latin-1",
byteorder=None):
warnings.warn(
"StataWriter is deprecated as of 0.10.0 and will be removed in a "
"future version. Use pandas.DataFrame.to_stata or "
"pandas.io.stata.StatWriter instead.",
FutureWarning)
self._convert_dates = convert_dates
# attach nobs, nvars, data, varlist, typlist
if data_util._is_using_pandas(data, None):
self._prepare_pandas(data)
elif data_util._is_array_like(data, None):
data = np.asarray(data)
if data_util._is_structured_ndarray(data):
self._prepare_structured_array(data)
else:
if convert_dates is not None:
raise ValueError("Not able to convert dates in a plain"
" ndarray.")
self._prepare_ndarray(data)
else: # pragma : no cover
raise ValueError("Type %s for data not understood" % type(data))
if byteorder is None:
byteorder = sys.byteorder
self._byteorder = _set_endianness(byteorder)
self._encoding = encoding
self._file = get_file_obj(fname, 'wb', encoding)
def save_pickle(obj, fname):
"""
Save the object to file via pickling.
Parameters
----------
fname : str
Filename to pickle to
"""
with get_file_obj(fname, 'wb') as fout:
cPickle.dump(obj, fout, protocol=-1)
def save_pickle(obj, fname):
"""
Save the object to file via pickling.
Parameters
----------
fname : str
Filename to pickle to
"""
with get_file_obj(fname, 'wb') as fout:
cPickle.dump(obj, fout, protocol=-1)
def save_pickle(obj, fname):
"""
Save the object to file via pickling.
Parameters
----------
fname : {str, pathlib.Path}
Filename to pickle to
"""
import pickle
with get_file_obj(fname, "wb") as fout:
pickle.dump(obj, fout, protocol=-1)
def load_pickle(fname):
"""
Load a previously saved object from file
Parameters
----------
fname : str
Filename to unpickle
Notes
-----
This method can be used to load *both* models and results.
"""
with get_file_obj(fname, 'rb') as fin:
return cPickle.load(fin)
def load_pickle(fname):
"""
Load a previously saved object from file
Parameters
----------
fname : str
Filename to unpickle
Notes
-----
This method can be used to load *both* models and results.
"""
with get_file_obj(fname, 'rb') as fin:
return cPickle.load(fin)
def load_pickle(fname):
"""
Load a previously saved object
.. warning::
Loading pickled models is not secure against erroneous or maliciously
constructed data. Never unpickle data received from an untrusted or
unauthenticated source.
Parameters
----------
fname : str
Filename to unpickle
Notes
-----
This method can be used to load *both* models and results.
"""
import pickle
with get_file_obj(fname, 'rb') as fin:
return pickle.load(fin)
def savetxt(fname, X, names=None, fmt='%.18e', delimiter=' '):
"""
Save an array to a text file.
This is just a copy of numpy.savetxt patched to support structured arrays
or a header of names. Does not include py3 support now in savetxt.
Parameters
----------
fname : filename or file handle
If the filename ends in ``.gz``, the file is automatically saved in
compressed gzip format. `loadtxt` understands gzipped files
transparently.
X : array_like
Data to be saved to a text file.
names : list, optional
If given names will be the column header in the text file. If None and
X is a structured or recarray then the names are taken from
X.dtype.names.
fmt : str or sequence of strs
A single format (%10.5f), a sequence of formats, or a
multi-format string, e.g. 'Iteration %d -- %10.5f', in which
case `delimiter` is ignored.
delimiter : str
Character separating columns.
See Also
--------
save : Save an array to a binary file in NumPy ``.npy`` format
savez : Save several arrays into a ``.npz`` compressed archive
Notes
-----
Further explanation of the `fmt` parameter
(``%[flag]width[.precision]specifier``):
flags:
``-`` : left justify
``+`` : Forces to preceed result with + or -.
``0`` : Left pad the number with zeros instead of space (see width).
width:
Minimum number of characters to be printed. The value is not truncated
if it has more characters.
precision:
- For integer specifiers (eg. ``d,i,o,x``), the minimum number of
digits.
- For ``e, E`` and ``f`` specifiers, the number of digits to print
after the decimal point.
- For ``g`` and ``G``, the maximum number of significant digits.
- For ``s``, the maximum number of characters.
specifiers:
``c`` : character
``d`` or ``i`` : signed decimal integer
``e`` or ``E`` : scientific notation with ``e`` or ``E``.
``f`` : decimal floating point
``g,G`` : use the shorter of ``e,E`` or ``f``
``o`` : signed octal
``s`` : string of characters
``u`` : unsigned decimal integer
``x,X`` : unsigned hexadecimal integer
This explanation of ``fmt`` is not complete, for an exhaustive
specification see [1]_.
References
----------
.. [1] `Format Specification Mini-Language
<http://docs.python.org/library/string.html#
format-specification-mini-language>`_, Python Documentation.
Examples
--------
>>> savetxt('test.out', x, delimiter=',') # x is an array
>>> savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays
>>> savetxt('test.out', x, fmt='%1.4e') # use exponential notation
"""
with get_file_obj(fname, 'w') as fh:
X = np.asarray(X)
# Handle 1-dimensional arrays
if X.ndim == 1:
# Common case -- 1d array of numbers
if X.dtype.names is None:
X = np.atleast_2d(X).T
ncol = 1
# Complex dtype -- each field indicates a separate column
else:
ncol = len(X.dtype.descr)
else:
ncol = X.shape[1]
# `fmt` can be a string with multiple insertion points or a list of formats.
# E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
if isinstance(fmt, (list, tuple)):
if len(fmt) != ncol:
raise AttributeError('fmt has wrong shape. %s' % str(fmt))
format = delimiter.join(fmt)
elif isinstance(fmt, string_types):
if fmt.count('%') == 1:
fmt = [
fmt,
] * ncol
format = delimiter.join(fmt)
elif fmt.count('%') != ncol:
raise AttributeError(
'fmt has wrong number of %% formats. %s' % fmt)
else:
format = fmt
# handle names
if names is None and X.dtype.names:
names = X.dtype.names
if names is not None:
fh.write(delimiter.join(names) + '\n')
for row in X:
fh.write(format % tuple(row) + '\n')
def savetxt(fname, X, names=None, fmt='%.18e', delimiter=' '):
"""
Save an array to a text file.
This is just a copy of numpy.savetxt patched to support structured arrays
or a header of names. Does not include py3 support now in savetxt.
Parameters
----------
fname : filename or file handle
If the filename ends in ``.gz``, the file is automatically saved in
compressed gzip format. `loadtxt` understands gzipped files
transparently.
X : array_like
Data to be saved to a text file.
names : list, optional
If given names will be the column header in the text file. If None and
X is a structured or recarray then the names are taken from
X.dtype.names.
fmt : str or sequence of strs
A single format (%10.5f), a sequence of formats, or a
multi-format string, e.g. 'Iteration %d -- %10.5f', in which
case `delimiter` is ignored.
delimiter : str
Character separating columns.
See Also
--------
save : Save an array to a binary file in NumPy ``.npy`` format
savez : Save several arrays into a ``.npz`` compressed archive
Notes
-----
Further explanation of the `fmt` parameter
(``%[flag]width[.precision]specifier``):
flags:
``-`` : left justify
``+`` : Forces to preceed result with + or -.
``0`` : Left pad the number with zeros instead of space (see width).
width:
Minimum number of characters to be printed. The value is not truncated
if it has more characters.
precision:
- For integer specifiers (eg. ``d,i,o,x``), the minimum number of
digits.
- For ``e, E`` and ``f`` specifiers, the number of digits to print
after the decimal point.
- For ``g`` and ``G``, the maximum number of significant digits.
- For ``s``, the maximum number of characters.
specifiers:
``c`` : character
``d`` or ``i`` : signed decimal integer
``e`` or ``E`` : scientific notation with ``e`` or ``E``.
``f`` : decimal floating point
``g,G`` : use the shorter of ``e,E`` or ``f``
``o`` : signed octal
``s`` : string of characters
``u`` : unsigned decimal integer
``x,X`` : unsigned hexadecimal integer
This explanation of ``fmt`` is not complete, for an exhaustive
specification see [1]_.
References
----------
.. [1] `Format Specification Mini-Language
<http://docs.python.org/library/string.html#
format-specification-mini-language>`_, Python Documentation.
Examples
--------
>>> savetxt('test.out', x, delimiter=',') # x is an array
>>> savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays
>>> savetxt('test.out', x, fmt='%1.4e') # use exponential notation
"""
with get_file_obj(fname, 'w') as fh:
X = np.asarray(X)
# Handle 1-dimensional arrays
if X.ndim == 1:
# Common case -- 1d array of numbers
if X.dtype.names is None:
X = np.atleast_2d(X).T
ncol = 1
# Complex dtype -- each field indicates a separate column
else:
ncol = len(X.dtype.descr)
else:
ncol = X.shape[1]
# `fmt` can be a string with multiple insertion points or a list of formats.
# E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
if isinstance(fmt, (list, tuple)):
if len(fmt) != ncol:
raise AttributeError('fmt has wrong shape. %s' % str(fmt))
format = delimiter.join(fmt)
elif isinstance(fmt, string_types):
if fmt.count('%') == 1:
fmt = [fmt, ]*ncol
format = delimiter.join(fmt)
elif fmt.count('%') != ncol:
raise AttributeError('fmt has wrong number of %% formats. %s'
% fmt)
else:
format = fmt
# handle names
if names is None and X.dtype.names:
names = X.dtype.names
if names is not None:
fh.write(delimiter.join(names) + '\n')
for row in X:
fh.write(format % tuple(row) + '\n')