def _check_extension_sheets(self, ext):
path = '__tmp_to_excel_from_excel_sheets__.' + ext
self.frame['A'][:5] = nan
self.frame.to_excel(path, 'test1')
self.frame.to_excel(path, 'test1', cols=['A', 'B'])
self.frame.to_excel(path, 'test1', header=False)
self.frame.to_excel(path, 'test1', index=False)
# Test writing to separate sheets
writer = ExcelWriter(path)
self.frame.to_excel(writer, 'test1')
self.tsframe.to_excel(writer, 'test2')
writer.save()
reader = ExcelFile(path)
recons = reader.parse('test1', index_col=0)
tm.assert_frame_equal(self.frame, recons)
recons = reader.parse('test2', index_col=0)
tm.assert_frame_equal(self.tsframe, recons)
np.testing.assert_equal(2, len(reader.sheet_names))
np.testing.assert_equal('test1', reader.sheet_names[0])
np.testing.assert_equal('test2', reader.sheet_names[1])
os.remove(path)
def _check_extension_sheets(self, ext):
path = "__tmp_to_excel_from_excel_sheets__." + ext
self.frame["A"][:5] = nan
self.frame.to_excel(path, "test1")
self.frame.to_excel(path, "test1", cols=["A", "B"])
self.frame.to_excel(path, "test1", header=False)
self.frame.to_excel(path, "test1", index=False)
# Test writing to separate sheets
writer = ExcelWriter(path)
self.frame.to_excel(writer, "test1")
self.tsframe.to_excel(writer, "test2")
writer.save()
reader = ExcelFile(path)
recons = reader.parse("test1", index_col=0)
tm.assert_frame_equal(self.frame, recons)
recons = reader.parse("test2", index_col=0)
tm.assert_frame_equal(self.tsframe, recons)
np.testing.assert_equal(2, len(reader.sheet_names))
np.testing.assert_equal("test1", reader.sheet_names[0])
np.testing.assert_equal("test2", reader.sheet_names[1])
os.remove(path)
def _check_extension_sheets(self, ext):
path = '__tmp_to_excel_from_excel_sheets__.' + ext
self.frame['A'][:5] = nan
self.frame.to_excel(path,'test1')
self.frame.to_excel(path,'test1', cols=['A', 'B'])
self.frame.to_excel(path,'test1', header=False)
self.frame.to_excel(path,'test1', index=False)
# Test writing to separate sheets
writer = ExcelWriter(path)
self.frame.to_excel(writer,'test1')
self.tsframe.to_excel(writer,'test2')
writer.save()
reader = ExcelFile(path)
recons = reader.parse('test1',index_col=0)
tm.assert_frame_equal(self.frame, recons)
recons = reader.parse('test2',index_col=0)
tm.assert_frame_equal(self.tsframe, recons)
np.testing.assert_equal(2, len(reader.sheet_names))
np.testing.assert_equal('test1', reader.sheet_names[0])
np.testing.assert_equal('test2', reader.sheet_names[1])
os.remove(path)
def export_to(self, file_path, batchsize=100):
self.xls_writer = ExcelWriter(file_path)
# get record count
record_count = self._query_mongo(count=True)
# query in batches and for each batch create an XLSDataFrameWriter and
# write to existing xls_writer object
start = 0
header = True
while start < record_count:
cursor = self._query_mongo(self.filter_query, start=start,
limit=batchsize)
data = self._format_for_dataframe(cursor)
# write all cursor's data to their respective sheets
for section_name, section in self.sections.iteritems():
records = data[section_name]
# TODO: currently ignoring nested repeats so ignore sections that have 0 records
if len(records) > 0:
columns = section["columns"] + self.EXTRA_COLUMNS
writer = XLSDataFrameWriter(records, columns)
writer.write_to_excel(self.xls_writer, section_name,
header=header, index=False)
header = False
# increment counter(s)
start += batchsize
self.xls_writer.save()
def export_to(self, file_path, batchsize=1000):
self.xls_writer = ExcelWriter(file_path)
# get record count
record_count = self._query_mongo(count=True)
# query in batches and for each batch create an XLSDataFrameWriter and
# write to existing xls_writer object
start = 0
header = True
while start < record_count:
cursor = self._query_mongo(self.filter_query, start=start,
limit=batchsize)
data = self._format_for_dataframe(cursor)
# write all cursor's data to their respective sheets
for section_name, section in self.sections.iteritems():
records = data[section_name]
# TODO: currently ignoring nested repeats so ignore sections that have 0 records
if len(records) > 0:
# use a different group delimiter if needed
columns = section["columns"]
if self.group_delimiter != DEFAULT_GROUP_DELIMITER:
columns = [self.group_delimiter.join(col.split("/")) for col in columns ]
columns = columns + self.EXTRA_COLUMNS
writer = XLSDataFrameWriter(records, columns)
writer.write_to_excel(self.xls_writer, section_name,
header=header, index=False)
header = False
# increment counter(s)
start += batchsize
time.sleep(0.1)
self.xls_writer.save()
def export_to(self, file_path):
self.xls_writer = ExcelWriter(file_path)
# query in batches and for each batch create an XLSDataFrameWriter and
# write to existing xls_writer object
# get records from mongo - do this on export so we can batch if we
# choose to, as we should
cursor = self._query_mongo(self.filter_query)
data = self._format_for_dataframe(cursor)
#TODO: batching will not work as expected since indexes are calculated
# based the current batch, a new batch will re-calculate indexes and if
# they are going into the same excel file, we'll have duplicates
# possible solution - keep track of the last index from each section
# write all cursor's data to different sheets
# TODO: for every repeat, the index should be re-calculated
for section in self.sections:
# TODO: currently ignoring nested repeat data which will have no
# records
records = data[section["name"]]
if len(records) > 0:
section_name = section["name"]
columns = section["columns"] + self.EXTRA_COLUMNS
writer = XLSDataFrameWriter(records, columns)
writer.write_to_excel(self.xls_writer, section_name,
header=True, index=False)
self.xls_writer.save()
def to_excel(self, path, na_rep=''):
"""
Write each DataFrame in Panel to a separate excel sheet
Parameters
----------
excel_writer : string or ExcelWriter object
File path or existing ExcelWriter
na_rep : string, default ''
Missing data rep'n
"""
from pandas.io.parsers import ExcelWriter
writer = ExcelWriter(path)
for item, df in self.iteritems():
name = str(item)
df.to_excel(writer, name, na_rep=na_rep)
writer.save()
def to_excel(self, path, na_rep=''):
"""
Write each DataFrame in Panel to a separate excel sheet
Parameters
----------
excel_writer : string or ExcelWriter object
File path or existing ExcelWriter
na_rep : string, default ''
Missing data representation
"""
from pandas.io.parsers import ExcelWriter
writer = ExcelWriter(path)
for item, df in self.iteritems():
name = str(item)
df.to_excel(writer, name, na_rep=na_rep)
writer.save()
def export_to(self, file_path, batchsize=1000):
self.xls_writer = ExcelWriter(file_path)
# get record count
record_count = self._query_mongo(count=True)
# query in batches and for each batch create an XLSDataFrameWriter and
# write to existing xls_writer object
start = 0
header = True
while start < record_count:
cursor = self._query_mongo(self.filter_query,
start=start,
limit=batchsize)
data = self._format_for_dataframe(cursor)
# write all cursor's data to their respective sheets
for section_name, section in self.sections.iteritems():
records = data[section_name]
# TODO: currently ignoring nested repeats
# so ignore sections that have 0 records
if len(records) > 0:
# use a different group delimiter if needed
columns = section["columns"]
if self.group_delimiter != DEFAULT_GROUP_DELIMITER:
columns = [
self.group_delimiter.join(col.split("/"))
for col in columns
]
columns = columns + self.EXTRA_COLUMNS
writer = XLSDataFrameWriter(records, columns)
writer.write_to_excel(self.xls_writer,
section_name,
header=header,
index=False)
header = False
# increment counter(s)
start += batchsize
time.sleep(0.1)
self.xls_writer.save()
# big = big.drop('AnnStaticRet', 1)
# big = big.drop('AnnCapitalRet', 1)
# big['AnnStaticRet'] = new_ind.AnnStaticRet.values
# big['AnnCapitalRet'] = new_ind.AnnCapitalRet.values
today_str = str(str(month) + str(day) + str(year))
big = big.rename(columns={'Last': 'OptionPrice', 'industry': 'Industry'})
xlsx = '.xlsx'
csv = '.csv'
file_name = 'All_covered_call' + today_str
sectors = big.Sector.unique().astype(str)
name_xl = file_name + xlsx
writer = ExcelWriter(name_xl)
big.to_excel(writer, sheet_name='All Sectors')
summary = big.groupby(['Sector', 'Industry']).mean()
summary.to_excel(writer, sheet_name='Sector Summary')
for i in sectors:
to_save = big[big.Sector == i]
name = i.replace('/', '-')
to_save.to_excel(writer, sheet_name=name)
writer.save()
name_cs = file_name + csv
big.to_csv(name_cs)
class XLSDataFrameBuilder(AbstractDataFrameBuilder):
"""
Generate structures from mongo and DataDictionary for a DataFrameXLSWriter
This builder can choose to query the data in batches and write to a single
ExcelWriter object using multiple instances of DataFrameXLSWriter
"""
INDEX_COLUMN = u"_index"
PARENT_TABLE_NAME_COLUMN = u"_parent_table_name"
PARENT_INDEX_COLUMN = u"_parent_index"
EXTRA_COLUMNS = [INDEX_COLUMN, PARENT_TABLE_NAME_COLUMN,
PARENT_INDEX_COLUMN]
SHEET_NAME_MAX_CHARS = 30
XLS_SHEET_COUNT_LIMIT = 255
XLS_COLUMN_COUNT_MAX = 255
CURRENT_INDEX_META = 'current_index'
def __init__(self, username, id_string, filter_query=None):
super(XLSDataFrameBuilder, self).__init__(username, id_string,
filter_query)
def _setup(self):
super(XLSDataFrameBuilder, self)._setup()
# need to split columns, with repeats in individual sheets and
# everything else on the default sheet
self._generate_sections()
def export_to(self, file_path, batchsize=100):
self.xls_writer = ExcelWriter(file_path)
# get record count
record_count = self._query_mongo(count=True)
# query in batches and for each batch create an XLSDataFrameWriter and
# write to existing xls_writer object
start = 0
header = True
while start < record_count:
cursor = self._query_mongo(self.filter_query, start=start,
limit=batchsize)
data = self._format_for_dataframe(cursor)
# write all cursor's data to their respective sheets
for section_name, section in self.sections.iteritems():
records = data[section_name]
# TODO: currently ignoring nested repeats so ignore sections that have 0 records
if len(records) > 0:
columns = section["columns"] + self.EXTRA_COLUMNS
writer = XLSDataFrameWriter(records, columns)
writer.write_to_excel(self.xls_writer, section_name,
header=header, index=False)
header = False
# increment counter(s)
start += batchsize
self.xls_writer.save()
def _format_for_dataframe(self, cursor):
"""
Format each record for consumption by a dataframe
returns a dictionary with the key being the name of the sheet, and values
a list of dicts to feed into a DataFrame
"""
data = dict((section_name, []) for section_name in self.sections.keys())
default_section = self.sections[self.survey_name]
default_columns = default_section["columns"]
for record in cursor:
# from record, we'll end up with multiple records, one for each
# section we have
# add records for the default section
self._add_data_for_section(data[self.survey_name],
record, default_columns, self.survey_name)
parent_index = default_section[self.CURRENT_INDEX_META]
for sheet_name, section in self.sections.iteritems():
# skip default section i.e survey name
if sheet_name != self.survey_name:
xpath = section["xpath"]
columns = section["columns"]
# TODO: handle nested repeats -ignoring nested repeats for
# now which will not be in the top level record, perhaps
# nest sections as well so we can recurs in and get them
if record.has_key(xpath):
repeat_records = record[xpath]
num_repeat_records = len(repeat_records)
for repeat_record in repeat_records:
self._add_data_for_section(data[sheet_name],
repeat_record, columns, sheet_name,
parent_index, self.survey_name)
return data
def _add_data_for_section(self, data_section, record, columns, section_name,
parent_index = -1, parent_table_name = None):
data_section.append({})
self.sections[section_name][self.CURRENT_INDEX_META] += 1
index = self.sections[section_name][self.CURRENT_INDEX_META]
#data_section[len(data_section)-1].update(record) # we could simply do
# this but end up with duplicate data from repeats
# find any select multiple(s) and add additional columns to record
record = self._split_select_multiples(record, self.select_multiples)
# alt, precision
self._split_gps_fields(record, self.gps_fields)
for column in columns:
data_value = None
try:
data_value = record[column]
except KeyError:
# a record may not have responses for some elements simply
# because they were not captured
pass
data_section[len(data_section)-1].update({column: data_value})
data_section[len(data_section)-1].update({
XLSDataFrameBuilder.INDEX_COLUMN: index,
XLSDataFrameBuilder.PARENT_INDEX_COLUMN: parent_index,
XLSDataFrameBuilder.PARENT_TABLE_NAME_COLUMN: parent_table_name})
def _generate_sections(self):
"""
Split survey questions into separate sections for each xls sheet and
columns for each section
"""
# clear list
self.sections = OrderedDict()
self.survey_name, survey_xpath = survey_name_and_xpath_from_dd(self.dd)
# generate a unique and valid xls sheet name
self.survey_name = get_valid_sheet_name(self.survey_name,
self.sections.keys())
# setup the default section
self._create_section(self.survey_name, survey_xpath, False)
# dict of select multiple elements
self.select_multiples = {}
# get form elements to split repeats into separate section/sheets and
# everything else in the default section
for e in self.dd.get_survey_elements():
# check for a Section or sub-classes of
if isinstance(e, Section):
# always default to the main sheet
sheet_name = self.survey_name
# if a repeat we use its name
if isinstance(e, RepeatingSection):
sheet_name = e.name
sheet_name = get_valid_sheet_name(sheet_name,
self.sections.keys())
self._create_section(sheet_name, e.get_abbreviated_xpath(),
True)
# for each child add to survey_sections
for c in e.children:
if isinstance(c, Question) and not \
question_types_to_exclude(c.type)\
and not c.bind.get(u"type") == MULTIPLE_SELECT_BIND_TYPE:
self._add_column_to_section(sheet_name, c)
elif c.bind.get(u"type") == MULTIPLE_SELECT_BIND_TYPE:
self.select_multiples[c.get_abbreviated_xpath()] = \
[option.get_abbreviated_xpath() for option in
c.children]
# if select multiple, get its choices and make them
# columns
for option in c.children:
self._add_column_to_section(sheet_name, option)
# split gps fields within this section
if c.bind.get(u"type") == GEOPOINT_BIND_TYPE:
# add columns for geopoint components
for xpath in\
self.dd.get_additional_geopoint_xpaths(
c.get_abbreviated_xpath()):
self._add_column_to_section(sheet_name, xpath)
self.get_exceeds_xls_limits()
def get_exceeds_xls_limits(self):
if not hasattr(self, "exceeds_xls_limits"):
self.exceeds_xls_limits = False
if len(self.sections) > self.XLS_SHEET_COUNT_LIMIT:
self.exceeds_xls_limits = True
else:
for section in self.sections.itervalues():
if len(section["columns"]) > self.XLS_COLUMN_COUNT_MAX:
self.exceeds_xls_limits = True
break
return self.exceeds_xls_limits
def _create_section(self, section_name, xpath, is_repeat):
index = len(self.sections)
self.sections[section_name] = {"name": section_name, "xpath": xpath,
"columns": [], "is_repeat": is_repeat,
self.CURRENT_INDEX_META: 0}
def _add_column_to_section(self, sheet_name, column):
section = self.sections[sheet_name]
xpath = None
if isinstance(column, SurveyElement):
xpath = column.get_abbreviated_xpath()
elif isinstance(column, basestring):
xpath = column
assert(xpath)
# make sure column is not already in list
if xpath not in section["columns"]:
section["columns"].append(xpath)
class XLSDataFrameBuilder(AbstractDataFrameBuilder):
"""
Generate structures from mongo and DataDictionary for a DataFrameXLSWriter
This builder can choose to query the data in batches and write to a single
ExcelWriter object using multiple instances of DataFrameXLSWriter
"""
INDEX_COLUMN = u"_index"
PARENT_TABLE_NAME_COLUMN = u"_parent_table_name"
PARENT_INDEX_COLUMN = u"_parent_index"
EXTRA_COLUMNS = [
INDEX_COLUMN, PARENT_TABLE_NAME_COLUMN, PARENT_INDEX_COLUMN
]
SHEET_NAME_MAX_CHARS = 30
XLS_SHEET_COUNT_LIMIT = 255
XLS_COLUMN_COUNT_MAX = 255
CURRENT_INDEX_META = 'current_index'
def __init__(self,
username,
id_string,
filter_query=None,
group_delimiter=DEFAULT_GROUP_DELIMITER,
split_select_multiples=True):
super(XLSDataFrameBuilder,
self).__init__(username, id_string, filter_query,
group_delimiter, split_select_multiples)
def _setup(self):
super(XLSDataFrameBuilder, self)._setup()
# need to split columns, with repeats in individual sheets and
# everything else on the default sheet
self._generate_sections()
def export_to(self, file_path, batchsize=1000):
self.xls_writer = ExcelWriter(file_path)
# get record count
record_count = self._query_mongo(count=True)
# query in batches and for each batch create an XLSDataFrameWriter and
# write to existing xls_writer object
start = 0
header = True
while start < record_count:
cursor = self._query_mongo(self.filter_query,
start=start,
limit=batchsize)
data = self._format_for_dataframe(cursor)
# write all cursor's data to their respective sheets
for section_name, section in self.sections.iteritems():
records = data[section_name]
# TODO: currently ignoring nested repeats so ignore sections that have 0 records
if len(records) > 0:
# use a different group delimiter if needed
columns = section["columns"]
if self.group_delimiter != DEFAULT_GROUP_DELIMITER:
columns = [
self.group_delimiter.join(col.split("/"))
for col in columns
]
columns = columns + self.EXTRA_COLUMNS
writer = XLSDataFrameWriter(records, columns)
writer.write_to_excel(self.xls_writer,
section_name,
header=header,
index=False)
header = False
# increment counter(s)
start += batchsize
time.sleep(0.1)
self.xls_writer.save()
def _format_for_dataframe(self, cursor):
"""
Format each record for consumption by a dataframe
returns a dictionary with the key being the name of the sheet, and values
a list of dicts to feed into a DataFrame
"""
data = dict(
(section_name, []) for section_name in self.sections.keys())
main_section = self.sections[self.survey_name]
main_sections_columns = main_section["columns"]
for record in cursor:
# from record, we'll end up with multiple records, one for each
# section we have
# add records for the default section
self._add_data_for_section(data[self.survey_name], record,
main_sections_columns, self.survey_name)
parent_index = main_section[self.CURRENT_INDEX_META]
for sheet_name, section in self.sections.iteritems():
# skip default section i.e survey name
if sheet_name != self.survey_name:
xpath = section["xpath"]
columns = section["columns"]
# TODO: handle nested repeats -ignoring nested repeats for
# now which will not be in the top level record, perhaps
# nest sections as well so we can recurs in and get them
if record.has_key(xpath):
repeat_records = record[xpath]
num_repeat_records = len(repeat_records)
for repeat_record in repeat_records:
self._add_data_for_section(data[sheet_name],
repeat_record, columns,
sheet_name,
parent_index,
self.survey_name)
return data
def _add_data_for_section(self,
data_section,
record,
columns,
section_name,
parent_index=-1,
parent_table_name=None):
data_section.append({})
self.sections[section_name][self.CURRENT_INDEX_META] += 1
index = self.sections[section_name][self.CURRENT_INDEX_META]
#data_section[len(data_section)-1].update(record) # we could simply do
# this but end up with duplicate data from repeats
if self.split_select_multiples:
# find any select multiple(s) and add additional columns to record
record = self._split_select_multiples(record,
self.select_multiples)
# alt, precision
self._split_gps_fields(record, self.gps_fields)
for column in columns:
data_value = None
try:
data_value = record[column]
except KeyError:
# a record may not have responses for some elements simply
# because they were not captured
pass
data_section[len(data_section
) - 1].update(
{
self.group_delimiter.join(
column.split('/')) if self.group_delimiter != DEFAULT_GROUP_DELIMITER else column:
data_value
})
data_section[len(data_section) - 1].update({
XLSDataFrameBuilder.INDEX_COLUMN:
index,
XLSDataFrameBuilder.PARENT_INDEX_COLUMN:
parent_index,
XLSDataFrameBuilder.PARENT_TABLE_NAME_COLUMN:
parent_table_name
})
# add ADDITIONAL_COLUMNS
data_section[len(data_section) - 1].update(
dict([(column, record[column] if record.has_key(column) else None)
for column in self.ADDITIONAL_COLUMNS]))
def _generate_sections(self):
"""
Split survey questions into separate sections for each xls sheet and
columns for each section
"""
# clear list
self.sections = OrderedDict()
# dict of select multiple elements
self.select_multiples = {}
survey_element = self.dd.survey
self.survey_name = get_valid_sheet_name(survey_element.name,
self.sections.keys())
self._create_section(self.survey_name,
survey_element.get_abbreviated_xpath(), False)
# build sections
self._build_sections_recursive(self.survey_name, self.dd.get_survey())
for section_name in self.sections:
self.sections[section_name]['columns'] += self.ADDITIONAL_COLUMNS
self.get_exceeds_xls_limits()
def _build_sections_recursive(self,
section_name,
element,
is_repeating=False):
"""Builds a section's children and recurses any repeating sections
to build those as a separate section
"""
for child in element.children:
# if a section, recurse
if isinstance(child, Section):
new_is_repeating = isinstance(child, RepeatingSection)
new_section_name = section_name
# if its repeating, build a new section
if new_is_repeating:
new_section_name = get_valid_sheet_name(
child.name, self.sections.keys())
self._create_section(new_section_name,
child.get_abbreviated_xpath(), True)
self._build_sections_recursive(new_section_name, child,
new_is_repeating)
else:
# add to survey_sections
if isinstance(child, Question) and not \
question_types_to_exclude(child.type)\
and not child.bind.get(u"type") == MULTIPLE_SELECT_BIND_TYPE:
self._add_column_to_section(section_name, child)
elif child.bind.get(u"type") == MULTIPLE_SELECT_BIND_TYPE:
self.select_multiples[child.get_abbreviated_xpath()] = \
[option.get_abbreviated_xpath() for option in
child.children]
# if select multiple, get its choices and make them
# columns
if self.split_select_multiples:
for option in child.children:
self._add_column_to_section(section_name, option)
else:
self._add_column_to_section(section_name, child)
# split gps fields within this section
if child.bind.get(u"type") == GEOPOINT_BIND_TYPE:
# add columns for geopoint components
for xpath in\
self.dd.get_additional_geopoint_xpaths(
child.get_abbreviated_xpath()):
self._add_column_to_section(section_name, xpath)
def get_exceeds_xls_limits(self):
if not hasattr(self, "exceeds_xls_limits"):
self.exceeds_xls_limits = False
if len(self.sections) > self.XLS_SHEET_COUNT_LIMIT:
self.exceeds_xls_limits = True
else:
for section in self.sections.itervalues():
if len(section["columns"]) > self.XLS_COLUMN_COUNT_MAX:
self.exceeds_xls_limits = True
break
return self.exceeds_xls_limits
def _create_section(self, section_name, xpath, is_repeat):
index = len(self.sections)
self.sections[section_name] = {
"name": section_name,
"xpath": xpath,
"columns": [],
"is_repeat": is_repeat,
self.CURRENT_INDEX_META: 0
}
def _add_column_to_section(self, sheet_name, column):
section = self.sections[sheet_name]
xpath = None
if isinstance(column, SurveyElement):
xpath = column.get_abbreviated_xpath()
elif isinstance(column, basestring):
xpath = column
assert (xpath)
# make sure column is not already in list
if xpath not in section["columns"]:
section["columns"].append(xpath)
# big = big.drop('AnnStaticRet', 1)
# big = big.drop('AnnCapitalRet', 1)
# big['AnnStaticRet'] = new_ind.AnnStaticRet.values
# big['AnnCapitalRet'] = new_ind.AnnCapitalRet.values
today_str = str(str(month) + str(day) + str(year))
big = big.rename(columns={'Last': 'OptionPrice', 'industry': 'Industry'})
xlsx = '.xlsx'
csv = '.csv'
file_name = 'All_covered_call' + today_str
sectors = big.Sector.unique().astype(str)
name_xl = file_name + xlsx
writer = ExcelWriter(name_xl)
big.to_excel(writer, sheet_name='All Sectors')
summary = big.groupby(['Sector', 'Industry']).mean()
summary.to_excel(writer, sheet_name='Sector Summary')
for i in sectors:
to_save = big[big.Sector == i]
name = i.replace('/', '-')
to_save.to_excel(writer, sheet_name=name)
writer.save()
name_cs = file_name + csv
big.to_csv(name_cs)
temp_frame2 = temp_frame2.dropna()
if month == 0:
final_frame = final_frame.join(temp_frame2, how='right')
else:
final_frame = pd.concat([final_frame, temp_frame2])
except:
pass
print 'Just finished ticker %s of %s' % (ticker, num_tickers)
today = str(
str(dt.datetime.now().month) + str(dt.datetime.now().day) +
str(dt.datetime.now().year))
file_name = 'NASDAQ_covered_call' + today
xlsx = '.xlsx'
csv = '.csv'
name_xl = file_name + xlsx
name_cs = file_name + csv
writer = ExcelWriter(file_name)
final_frame.to_excel(writer, sheet_name='Covered Call')
writer.save()
final_frame.to_csv(name_cs)
end_time = time()
elapsed_time = end_time - start_time
print elapsed_time
