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celinelee
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python_fn_calls

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_parse_json(financials_site)
pd.DataFrame.from_dict(temp["earningsChart"]["quarterly"])
pd.DataFrame.from_dict(temp["financialsChart"]["yearly"])
pd.DataFrame.from_dict(temp["financialsChart"]["quarterly"])
_parse_earnings_json(url, headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1)
requests.get(url, headers = headers)
resp.content.decode(encoding='utf-8', errors='strict')
row.startswith('root.App.main = ')
page_data.split('root.App.main = ', 1)
json.loads(page_data)
get_next_earnings_date(ticker)
_parse_earnings_json(new_url)
datetime.datetime.fromtimestamp(temp)
get_earnings_history(ticker)
_parse_earnings_json(url)
get_earnings_for_date(date, offset = 0, count = 1)
pd.Timestamp(date)
temp.strftime("%Y-%m-%d")
_parse_earnings_json(dated_url)
get_earnings_for_date(date, new_offset, earnings_count)
get_earnings_in_date_range(start_date, end_date)
pd.Timestamp(end_date)
pd.Timestamp(start_date)
pd.Timestamp(start_date)
datetime.timedelta(diff)
range(days_diff + 1)
d.strftime("%Y-%m-%d")
len(dates)
get_earnings_for_date(dates[i])
get_currencies(headers = {'User-agent': 'Mozilla/5.0'})
pd.read_html(requests.get(site, headers=headers)
get_futures(headers = {'User-agent': 'Mozilla/5.0'})
pd.read_html(requests.get(site, headers=headers)
get_undervalued_large_caps(headers = {'User-agent': 'Mozilla/5.0'})
pd.read_html(requests.get(site, headers=headers)
get_quote_data(ticker, headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1)
price (when applicable)
requests.get(site, headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1)
resp.json()
get_market_status()
get_quote_data("^dji")
get_premarket_price(ticker)
get_quote_data(ticker)
AssertionError("Premarket price not currently available.")
get_postmarket_price(ticker)
get_quote_data(ticker)
AssertionError("Postmarket price not currently available.")
get_company_info(ticker)
_parse_json(site)
info_frame.drop("companyOfficers", axis="index")
get_company_officers(ticker)
_parse_json(site)
pd.DataFrame.from_dict(json_info)
info_frame.set_index("name")
find_base(p, bases)
bases.items()
os.path.relpath(p, base_path)
and (r == '.' or r[0] != '.')
Lambda()
__init__(self, func_name, region, sess)
sess.client("lambda", region_name=region)
KLog.log("issue connecting to AWS %s" % str(e)
exit("[!] issue connecting to AWS: %s" % str(e)
disable(self)
KLog.log("no disable action for lambda function '%s', will delete instead" % self.func, "warning")
self.kill()
kill(self)
self.conn.delete_function(FunctionName=self.func)
str(e)
find("ResourceNotFoundException")
KLog.log("could not find function '%s', dequeueing task" % self.func)
KLog.log("could not delete function '%s', unknown error: %s" % str(e)
float(input("Entre com a temperatura que está agora: ")
print("A temperatura está boa")
print("A temperatura está quente\n Tome bastante líquido")
print("A temperatura está agradável")
print("A temperatura esta fria")
perform()
Poisson(Op)
make_node(self, x)
as_sparse_variable(x)
gof.Apply(self, [x], [x.type()
perform(self, node, inputs, outputs)
inputs (out,)
_is_sparse(x)
x.copy()
np.asarray(np.random.poisson(out[0].data)
eliminate_zeros()
grad(self, inputs, outputs_gradients)
infer_shape(self, fgraph, node, ins_shapes)
Poisson()
Binomial(Op)
__init__(self, format, dtype)
make_node(self, n, p, shape)
tensor.as_tensor_variable(n)
tensor.as_tensor_variable(p)
tensor.as_tensor_variable(shape)
SparseType(dtype=self.dtype, format=self.format)
perform(self, node, inputs, outputs)
inputs (out,)