def read_sas(filepath_or_buffer, format=None, index=None, encoding=None,
chunksize=None, iterator=False):
"""
Read SAS files stored as either XPORT or SAS7BDAT format files.
Parameters
----------
filepath_or_buffer : string or file-like object
Path to the SAS file.
format : string {'xport', 'sas7bdat'} or None
If None, file format is inferred from file extension. If 'xport' or
'sas7bdat', uses the corresponding format.
index : identifier of index column, defaults to None
Identifier of column that should be used as index of the DataFrame.
encoding : string, default is None
Encoding for text data. If None, text data are stored as raw bytes.
chunksize : int
Read file `chunksize` lines at a time, returns iterator.
iterator : bool, defaults to False
If True, returns an iterator for reading the file incrementally.
Returns
-------
DataFrame if iterator=False and chunksize=None, else SAS7BDATReader
or XportReader
"""
if format is None:
buffer_error_msg = ("If this is a buffer object rather "
"than a string name, you must specify "
"a format string")
filepath_or_buffer = _stringify_path(filepath_or_buffer)
if not isinstance(filepath_or_buffer, compat.string_types):
raise ValueError(buffer_error_msg)
fname = filepath_or_buffer.lower()
if fname.endswith(".xpt"):
format = "xport"
elif fname.endswith(".sas7bdat"):
format = "sas7bdat"
else:
raise ValueError("unable to infer format of SAS file")
if format.lower() == 'xport':
from pandas.io.sas.sas_xport import XportReader
reader = XportReader(filepath_or_buffer, index=index,
encoding=encoding,
chunksize=chunksize)
elif format.lower() == 'sas7bdat':
from pandas.io.sas.sas7bdat import SAS7BDATReader
reader = SAS7BDATReader(filepath_or_buffer, index=index,
encoding=encoding,
chunksize=chunksize)
else:
raise ValueError('unknown SAS format')
if iterator or chunksize:
return reader
data = reader.read()
reader.close()
return data
def read_sas(
filepath_or_buffer,
format=None,
index=None,
encoding=None,
chunksize=None,
iterator=False,
):
"""
Read SAS files stored as either XPORT or SAS7BDAT format files.
Parameters
----------
filepath_or_buffer : str, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be:
``file://localhost/path/to/table.sas``.
If you want to pass in a path object, pandas accepts any
``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method,
such as a file handler (e.g. via builtin ``open`` function)
or ``StringIO``.
format : string {'xport', 'sas7bdat'} or None
If None, file format is inferred from file extension. If 'xport' or
'sas7bdat', uses the corresponding format.
index : identifier of index column, defaults to None
Identifier of column that should be used as index of the DataFrame.
encoding : string, default is None
Encoding for text data. If None, text data are stored as raw bytes.
chunksize : int
Read file `chunksize` lines at a time, returns iterator.
iterator : bool, defaults to False
If True, returns an iterator for reading the file incrementally.
Returns
-------
DataFrame if iterator=False and chunksize=None, else SAS7BDATReader
or XportReader
"""
if format is None:
buffer_error_msg = (
"If this is a buffer object rather "
"than a string name, you must specify "
"a format string"
)
filepath_or_buffer = _stringify_path(filepath_or_buffer)
if not isinstance(filepath_or_buffer, str):
raise ValueError(buffer_error_msg)
fname = filepath_or_buffer.lower()
if fname.endswith(".xpt"):
format = "xport"
elif fname.endswith(".sas7bdat"):
format = "sas7bdat"
else:
raise ValueError("unable to infer format of SAS file")
if format.lower() == "xport":
from pandas.io.sas.sas_xport import XportReader
reader = XportReader(
filepath_or_buffer, index=index, encoding=encoding, chunksize=chunksize
)
elif format.lower() == "sas7bdat":
from pandas.io.sas.sas7bdat import SAS7BDATReader
reader = SAS7BDATReader(
filepath_or_buffer, index=index, encoding=encoding, chunksize=chunksize
)
else:
raise ValueError("unknown SAS format")
if iterator or chunksize:
return reader
data = reader.read()
reader.close()
return data