def _parse(flavor, io, match, attrs, encoding, **kwargs):
flavor = _validate_flavor(flavor)
compiled_match = re.compile(match) # you can pass a compiled regex here
# hack around python 3 deleting the exception variable
retained = None
for flav in flavor:
parser = _parser_dispatch(flav)
p = parser(io, compiled_match, attrs, encoding)
try:
tables = p.parse_tables()
except Exception as caught:
retained = caught
else:
break
else:
raise_with_traceback(retained)
ret = []
for table in tables:
try:
ret.append(_data_to_frame(data=table, **kwargs))
except EmptyDataError: # empty table
continue
return ret
def _parse(
flavor, io, match, header, index_col, skiprows, infer_types, parse_dates, tupleize_cols, thousands, attrs, encoding
):
flavor = _validate_flavor(flavor)
compiled_match = re.compile(match) # you can pass a compiled regex here
# hack around python 3 deleting the exception variable
retained = None
for flav in flavor:
parser = _parser_dispatch(flav)
p = parser(io, compiled_match, attrs, encoding)
try:
tables = p.parse_tables()
except Exception as caught:
retained = caught
else:
break
else:
raise_with_traceback(retained)
ret = []
for table in tables:
try:
ret.append(
_data_to_frame(table, header, index_col, skiprows, infer_types, parse_dates, tupleize_cols, thousands)
)
except StopIteration: # empty table
continue
return ret
def _parse(flavor, io, match, header, index_col, skiprows, parse_dates,
tupleize_cols, thousands, attrs, encoding):
flavor = _validate_flavor(flavor)
compiled_match = re.compile(match) # you can pass a compiled regex here
# hack around python 3 deleting the exception variable
retained = None
for flav in flavor:
parser = _parser_dispatch(flav)
p = parser(io, compiled_match, attrs, encoding)
try:
tables = p.parse_tables()
except Exception as caught:
retained = caught
else:
break
else:
raise_with_traceback(retained)
ret = []
for table in tables:
try:
ret.append(
_data_to_frame(data=table,
header=header,
index_col=index_col,
skiprows=skiprows,
parse_dates=parse_dates,
tupleize_cols=tupleize_cols,
thousands=thousands))
except StopIteration: # empty table
continue
return ret
def _parse(flavor, io, match, attrs, encoding, **kwargs):
flavor = _validate_flavor(flavor)
compiled_match = re.compile(match) # you can pass a compiled regex here
# hack around python 3 deleting the exception variable
retained = None
for flav in flavor:
parser = _parser_dispatch(flav)
p = parser(io, compiled_match, attrs, encoding)
try:
tables = p.parse_tables()
except Exception as caught:
retained = caught
else:
break
else:
raise_with_traceback(retained)
ret = []
for table in tables:
try:
ret.append(_data_to_frame(data=table, **kwargs))
except EmptyDataError: # empty table
continue
return ret
def _parse(flavor, io, match, header, index_col, skiprows, infer_types,
parse_dates, tupleize_cols, thousands, attrs, encoding):
flavor = _validate_flavor(flavor)
compiled_match = re.compile(match) # you can pass a compiled regex here
# hack around python 3 deleting the exception variable
retained = None
for flav in flavor:
parser = _parser_dispatch(flav)
p = parser(io, compiled_match, attrs, encoding)
try:
tables = p.parse_tables()
except Exception as caught:
retained = caught
else:
break
else:
raise_with_traceback(retained)
return [
_data_to_frame(table, header, index_col, skiprows, infer_types,
parse_dates, tupleize_cols, thousands)
for table in tables
]
def handle_success(self, exc_type, exc_value, traceback):
if self.regexp is not None:
val = str(exc_value)
if not self.regexp.search(val):
e = AssertionError('"%s" does not match "%s"' % (self.regexp.pattern, str(val)))
raise_with_traceback(e, traceback)
return True
def handle_success(self, exc_type, exc_value, traceback):
if self.regexp is not None:
val = str(exc_value)
if not self.regexp.search(val):
e = AssertionError('"%s" does not match "%s"' %
(self.regexp.pattern, str(val)))
raise_with_traceback(e, traceback)
return True
def init_ndarray(values, index, columns, dtype=None, copy=False):
# input must be a ndarray, list, Series, index
if isinstance(values, ABCSeries):
if columns is None:
if values.name is not None:
columns = [values.name]
if index is None:
index = values.index
else:
values = values.reindex(index)
# zero len case (GH #2234)
if not len(values) and columns is not None and len(columns):
values = np.empty((0, 1), dtype=object)
# we could have a categorical type passed or coerced to 'category'
# recast this to an arrays_to_mgr
if (is_categorical_dtype(getattr(values, 'dtype', None))
or is_categorical_dtype(dtype)):
if not hasattr(values, 'dtype'):
values = prep_ndarray(values, copy=copy)
values = values.ravel()
elif copy:
values = values.copy()
index, columns = _get_axes(len(values), 1, index, columns)
return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
elif (is_datetime64tz_dtype(values) or is_extension_array_dtype(values)):
# GH#19157
if columns is None:
columns = [0]
return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
# by definition an array here
# the dtypes will be coerced to a single dtype
values = prep_ndarray(values, copy=copy)
if dtype is not None:
if not is_dtype_equal(values.dtype, dtype):
try:
values = values.astype(dtype)
except Exception as orig:
e = ValueError("failed to cast to '{dtype}' (Exception "
"was: {orig})".format(dtype=dtype, orig=orig))
raise_with_traceback(e)
index, columns = _get_axes(*values.shape, index=index, columns=columns)
values = values.T
# if we don't have a dtype specified, then try to convert objects
# on the entire block; this is to convert if we have datetimelike's
# embedded in an object type
if dtype is None and is_object_dtype(values):
values = maybe_infer_to_datetimelike(values)
return create_block_manager_from_blocks([values], [columns, index])
def test_raise_with_traceback(self):
with pytest.raises(LookupError, match="error_text"):
try:
raise ValueError("THIS IS AN ERROR")
except ValueError as e:
e = LookupError("error_text")
raise_with_traceback(e)
with pytest.raises(LookupError, match="error_text"):
try:
raise ValueError("This is another error")
except ValueError:
e = LookupError("error_text")
_, _, traceback = sys.exc_info()
raise_with_traceback(e, traceback)
def test_raise_with_traceback(self):
with pytest.raises(LookupError, match="error_text"):
try:
raise ValueError("THIS IS AN ERROR")
except ValueError as e:
e = LookupError("error_text")
raise_with_traceback(e)
with pytest.raises(LookupError, match="error_text"):
try:
raise ValueError("This is another error")
except ValueError:
e = LookupError("error_text")
_, _, traceback = sys.exc_info()
raise_with_traceback(e, traceback)
def execute(self, *args, **kwargs):
try:
cur = self.con.cursor()
if kwargs:
cur.execute(*args, **kwargs)
else:
cur.execute(*args)
return cur
except Exception as e:
try:
self.con.rollback()
except Exception: # pragma: no cover
ex = DatabaseError("Execution failed on sql: %s\n%s\nunable to rollback" % (args[0], e))
raise_with_traceback(ex)
ex = DatabaseError("Execution failed on sql: %s" % args[0])
raise_with_traceback(ex)
def execute(self, *args, **kwargs):
try:
cur = self.con.cursor()
if kwargs:
cur.execute(*args, **kwargs)
else:
cur.execute(*args)
return cur
except Exception as e:
try:
self.con.rollback()
except Exception: # pragma: no cover
ex = DatabaseError(
"Execution failed on sql: %s\n%s\nunable to rollback" %
(args[0], e))
raise_with_traceback(ex)
ex = DatabaseError("Execution failed on sql: %s" % args[0])
raise_with_traceback(ex)
def _parse(flavor, io, match, attrs, encoding, displayed_only, **kwargs):
flavor = _validate_flavor(flavor)
compiled_match = re.compile(match) # you can pass a compiled regex here
# hack around python 3 deleting the exception variable
retained = None
for flav in flavor:
parser = _parser_dispatch(flav)
p = parser(io, compiled_match, attrs, encoding, displayed_only)
try:
tables = p.parse_tables()
except Exception as caught:
# if `io` is an io-like object, check if it's seekable
# and try to rewind it before trying the next parser
if hasattr(io, "seekable") and io.seekable():
io.seek(0)
elif hasattr(io, "seekable") and not io.seekable():
# if we couldn't rewind it, let the user know
raise ValueError(
"The flavor {} failed to parse your input. "
"Since you passed a non-rewindable file "
"object, we can't rewind it to try "
"another parser. Try read_html() with a "
"different flavor.".format(flav)
)
retained = caught
else:
break
else:
raise_with_traceback(retained)
ret = []
for table in tables:
try:
ret.append(_data_to_frame(data=table, **kwargs))
except EmptyDataError: # empty table
continue
return ret
def _parse(flavor, io, match, attrs, encoding, displayed_only, **kwargs):
flavor = _validate_flavor(flavor)
compiled_match = re.compile(match) # you can pass a compiled regex here
# hack around python 3 deleting the exception variable
retained = None
for flav in flavor:
parser = _parser_dispatch(flav)
p = parser(io, compiled_match, attrs, encoding, displayed_only)
try:
tables = p.parse_tables()
except Exception as caught:
# if `io` is an io-like object, check if it's seekable
# and try to rewind it before trying the next parser
if hasattr(io, 'seekable') and io.seekable():
io.seek(0)
elif hasattr(io, 'seekable') and not io.seekable():
# if we couldn't rewind it, let the user know
raise ValueError('The flavor {} failed to parse your input. '
'Since you passed a non-rewindable file '
'object, we can\'t rewind it to try '
'another parser. Try read_html() with a '
'different flavor.'.format(flav))
retained = caught
else:
break
else:
raise_with_traceback(retained)
ret = []
for table in tables:
try:
ret.append(_data_to_frame(data=table, **kwargs))
except EmptyDataError: # empty table
continue
return ret
def init_ndarray(values, index, columns, dtype=None, copy=False):
# input must be a ndarray, list, Series, index
if isinstance(values, ABCSeries):
if columns is None:
if values.name is not None:
columns = [values.name]
if index is None:
index = values.index
else:
values = values.reindex(index)
# zero len case (GH #2234)
if not len(values) and columns is not None and len(columns):
values = np.empty((0, 1), dtype=object)
# we could have a categorical type passed or coerced to 'category'
# recast this to an arrays_to_mgr
if (is_categorical_dtype(getattr(values, 'dtype', None))
or is_categorical_dtype(dtype)):
if not hasattr(values, 'dtype'):
values = prep_ndarray(values, copy=copy)
values = values.ravel()
elif copy:
values = values.copy()
index, columns = _get_axes(len(values), 1, index, columns)
return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
elif is_extension_array_dtype(values):
# GH#19157
if columns is None:
columns = [0]
return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
# by definition an array here
# the dtypes will be coerced to a single dtype
values = prep_ndarray(values, copy=copy)
if dtype is not None:
if not is_dtype_equal(values.dtype, dtype):
try:
values = values.astype(dtype)
except Exception as orig:
e = ValueError("failed to cast to '{dtype}' (Exception "
"was: {orig})".format(dtype=dtype, orig=orig))
raise_with_traceback(e)
index, columns = _get_axes(*values.shape, index=index, columns=columns)
values = values.T
# if we don't have a dtype specified, then try to convert objects
# on the entire block; this is to convert if we have datetimelike's
# embedded in an object type
if dtype is None and is_object_dtype(values):
if values.ndim == 2 and values.shape[0] != 1:
# transpose and separate blocks
dvals_list = [maybe_infer_to_datetimelike(row) for row in values]
for n in range(len(dvals_list)):
if isinstance(dvals_list[n], np.ndarray):
dvals_list[n] = dvals_list[n].reshape(1, -1)
from pandas.core.internals.blocks import make_block
# TODO: What about re-joining object columns?
block_values = [
make_block(dvals_list[n], placement=[n])
for n in range(len(dvals_list))
]
else:
datelike_vals = maybe_infer_to_datetimelike(values)
block_values = [datelike_vals]
else:
block_values = [values]
return create_block_manager_from_blocks(block_values, [columns, index])
def init_ndarray(values, index, columns, dtype=None, copy=False):
# input must be a ndarray, list, Series, index
if isinstance(values, ABCSeries):
if columns is None:
if values.name is not None:
columns = [values.name]
if index is None:
index = values.index
else:
values = values.reindex(index)
# zero len case (GH #2234)
if not len(values) and columns is not None and len(columns):
values = np.empty((0, 1), dtype=object)
# we could have a categorical type passed or coerced to 'category'
# recast this to an arrays_to_mgr
if (is_categorical_dtype(getattr(values, 'dtype', None)) or
is_categorical_dtype(dtype)):
if not hasattr(values, 'dtype'):
values = prep_ndarray(values, copy=copy)
values = values.ravel()
elif copy:
values = values.copy()
index, columns = _get_axes(len(values), 1, index, columns)
return arrays_to_mgr([values], columns, index, columns,
dtype=dtype)
elif (is_datetime64tz_dtype(values) or
is_extension_array_dtype(values)):
# GH#19157
if columns is None:
columns = [0]
return arrays_to_mgr([values], columns, index, columns,
dtype=dtype)
# by definition an array here
# the dtypes will be coerced to a single dtype
values = prep_ndarray(values, copy=copy)
if dtype is not None:
if not is_dtype_equal(values.dtype, dtype):
try:
values = values.astype(dtype)
except Exception as orig:
e = ValueError("failed to cast to '{dtype}' (Exception "
"was: {orig})".format(dtype=dtype,
orig=orig))
raise_with_traceback(e)
index, columns = _get_axes(*values.shape, index=index, columns=columns)
values = values.T
# if we don't have a dtype specified, then try to convert objects
# on the entire block; this is to convert if we have datetimelike's
# embedded in an object type
if dtype is None and is_object_dtype(values):
values = maybe_infer_to_datetimelike(values)
return create_block_manager_from_blocks([values], [columns, index])
