def calculateStatistics(self):
''' calculate spread statistics '''
res = {}
res['micro'] = rank(self.params['change'].sum(), self.change)
res['macro'] = rank(self.params['mktValue'].sum(), self.value)
res['last'] = self.params['mktValue'].sum()
return Series(res, name=self.name)
def calculateStatistics(self):
''' calculate spread statistics '''
res = {}
res['micro'] = rank(self.params['change'].sum(),self.change)
res['macro'] = rank(self.params['mktValue'].sum(), self.value)
res['last'] = self.params['mktValue'].sum()
return Series(res,name=self.name)
def calculateStatistics(self, other=None):
''' calculate spread statistics, save internally '''
res = {}
res['micro'] = rank(self.returns[-1], self.returns)
res['macro'] = rank(self.value[-1], self.value)
res['last'] = self.value[-1]
if other is not None:
res['corr'] = self.returns.corr(returns(other))
return Series(res, name=self.name)
def calculateStatistics(self,other=None):
''' calculate spread statistics, save internally '''
res = {}
res['micro'] = rank(self.returns[-1],self.returns)
res['macro'] = rank(self.value[-1], self.value)
res['last'] = self.value[-1]
if other is not None:
res['corr'] = self.returns.corr(returns(other))
return Series(res,name=self.name)
if specific_subjects:
df = df.loc[df['Subjects'].apply(offers_subjects, subjects=specific_subjects)]
weights = [1 for x in range(len(df.columns) - 1)]
# Get weights from sliders
weights_expander = st.beta_expander('Change Weights')
with weights_expander:
weights_columns = st.beta_columns(int((len(df.columns) - 1) / 2))
for ind, col in enumerate(df.columns):
if col == 'Subjects':
continue
weights[ind] = weights_columns[int(ind/2)].slider(f'{col} Weighting', max_value=10, step=1, value=1, key=str(ind))
# Rank the universities
df = rank(df, weights)
# Set columns to show on table
columns = ['Rank',
'% Satisfied with Teaching',
'% Satisfied with Course',
'% Satisfied with Assessment',
'Continuation %',
'% Graduates in High Skilled Work',
'Applications to Acceptance (%)',
'Student/Staff Ratio',
'Average Salary',
'Academic Services Expenditure per Student',
'Facilities Expenditure per Student']
df = df[columns]
def ranking(api):
fc.rank(api)
def main():
functions.clear_screen()
rank = functions.open_rank()
print(functions.get_info())
opc_usr = int(input("[0 a 5] >>> "))
if opc_usr == 1:
# NOTE: listar os times em ordem alfabetica
print("\n--- Ordem alfabetica:")
sorted_rank = sorted(rank)
functions.rank(0, len(rank), sorted_rank)
print("---\n")
elif opc_usr == 2:
# NOTE: todas as 20 colocacoes
print("\n--- 20 primeiros colocados:")
functions.rank(0, 20, rank)
print("---\n")
elif opc_usr == 3:
# NOTE: pesquisar por nome
print("\n--- Pesquisar por nome:")
print(
"- Digite o nome (ou parte dele) do time a ser pesquisado [sem acentuaçao]:"
)
search = str(input("- >>> ")).upper().strip()
teams_found = []
for team in rank:
if search in team:
teams_found.append(team)
if len(teams_found) > 0:
for team in teams_found:
index = rank.index(team)
print(f"- {index+1:>2}°\t{rank[index]}")
else:
print("Este time não consta entre os 20 classificados..."
"Verifique se o nome foi digitado corretamente.")
print("---\n")
elif opc_usr == 4:
# NOTE: apenas as 4 ultimas colocacoes
print("\n--- 4 ultimas colocacoes:")
lenght = len(rank)
functions.rank(lenght - 4, lenght, rank)
print("---\n")
elif opc_usr == 5:
# NOTE: apenas as 5 primeiras colocacoes
print("\n--- 5 primeiras colocacoes:")
functions.rank(0, 5, rank)
print("---\n")
elif opc_usr == 0:
print("Saindo imediatamente...")
exit()
else:
print("ERRO: opcao invalida, tente novamente")
input("Para continuar digite ENTER: ")
main()