def iget_records(custom_headers=None, **keywords):
"""
Obtain a generator of a list of records from an excel source
It is similiar to :meth:`pyexcel.get_records` but it has less memory
footprint but requires the headers to be in the first row. And the
data matrix should be of equal length. It should consume less memory
and should work well with large files.
"""
sheet_stream = sources.get_sheet_stream(on_demand=True, **keywords)
headers = None
for row_index, row in enumerate(sheet_stream.payload):
if row_index == 0:
headers = row
else:
if custom_headers:
# custom order
tmp_dict = dict(
zip_longest(headers, row, fillvalue=constants.DEFAULT_NA))
ordered_dict = OrderedDict()
for name in custom_headers:
ordered_dict[name] = tmp_dict[name]
yield ordered_dict
else:
# default order
yield OrderedDict(
zip_longest(headers, row, fillvalue=constants.DEFAULT_NA))
def yield_dict_to_array(the_dict, with_keys=True):
"""Convert a dictionary of columns to an array
The example dict is::
{
"Column 1": [1, 2, 3],
"Column 2": [5, 6, 7, 8],
"Column 3": [9, 10, 11, 12, 13],
}
The output will be::
[
["Column 1", "Column 2", "Column 3"],
[1, 5, 9],
[2, 6, 10],
[3, 7, 11],
['', 8, 12],
['', '', 13]
]
:param dict the_dict: the dictionary to be converted.
:param bool with_keys: to write the keys as the first row or not
"""
keys = the_dict.keys()
if not PY2:
keys = list(keys)
if not isinstance(the_dict, OrderedDict):
keys = sorted(keys)
if with_keys:
yield keys
sorted_values = (the_dict[key] for key in keys)
for row in zip_longest(*sorted_values, fillvalue=''):
yield list(row)
def row_iterator(self):
keys = self._native_sheet.keys()
if not PY2:
keys = list(keys)
if not isinstance(self._native_sheet, OrderedDict):
keys = sorted(keys)
if self._keywords.get('with_keys', True):
yield keys
sorted_values = (self._native_sheet[key] for key in keys)
for row in zip_longest(*sorted_values, fillvalue=constants.DEFAULT_NA):
yield row
def iget_records(**keywords):
"""Obtain a generator of a list of records from an excel source
It is similiar to :meth:`pyexcel.get_records` but it has less memory
footprint but requires the headers to be in the first row. And the
data matrix should be of equal length. It should consume less memory
and should work well with large files.
"""
sheet_stream = sources.get_sheet_stream(**keywords)
headers = None
for row_index, row in enumerate(sheet_stream.payload):
if row_index == 0:
headers = row
else:
yield dict(zip_longest(headers, row,
fillvalue=constants.DEFAULT_NA))
def iget_records(**keywords):
"""
Obtain a generator of a list of records from an excel source
It is similiar to :meth:`pyexcel.get_records` but it has less memory
footprint but requires the headers to be in the first row. And the
data matrix should be of equal length. It should consume less memory
and should work well with large files.
"""
sheet_stream = sources.get_sheet_stream(on_demand=True, **keywords)
headers = None
for row_index, row in enumerate(sheet_stream.payload):
if row_index == 0:
headers = row
else:
yield dict(zip_longest(headers, row,
fillvalue=constants.DEFAULT_NA))
def row_iterator(self):
keys = self._native_sheet.keys()
if not PY2:
keys = list(keys)
if not isinstance(self._native_sheet, OrderedDict):
keys = sorted(keys)
if self._keywords.get('with_keys', True):
yield keys
if isinstance(self._native_sheet[keys[0]], list):
sorted_values = (self._native_sheet[key] for key in keys)
for row in zip_longest(
*sorted_values,
fillvalue=constants.DEFAULT_NA):
yield row
else:
row = [self._native_sheet[key] for key in keys]
yield row