def main():
try:
sheet_name = 'Index'
df_temp1 = df_new.parse(sheet_name)
df_temp2 = df_old.parse(sheet_name)
df_temp1 = df_temp1[df_temp1['Index'] == 'Equity']
df_temp2 = df_temp2[df_temp2['Index'] == 'Equity']
sheet_names_new = df_temp1['Unnamed: 1'].tolist()
sheet_names_old = df_temp2['Unnamed: 1'].tolist()
writer = pd.ExcelWriter(output_file, mode='w')
for name in sheet_names_new:
if name in sheet_names_old:
print(name)
df1 = df_new.parse(name)
df2 = df_old.parse(name)
date1 = f.date_extract(df1)
month_date1 = str(date1.month)+'_'+str(date1.day)
logging.debug(" Month df1 : %s",month_date1)
date2 = f.date_extract(df2)
month_date2 = str(date2.month)+'_'+str(date2.day)
logging.debug(" Month df2 : %s",month_date2)
assert(date1.month - date2.month == 1)
logging.debug("Assertion correct")
dftmp1, qty1, mkt1, pnav1 = f.create_df(df1,month_date1)
dftmp2, qty2, mkt2, pnav2 = f.create_df(df2,month_date2)
logging.debug("new df created")
dftmpx1 = dftmp1[dftmp1['INDUSTRY'].notna()]
logger.debug("df1_shape : %s",dftmpx1.shape)
dftmpx2 = dftmp2[dftmp2['INDUSTRY'].notna()]
logger.debug("df2_shape : %s",dftmpx2.shape)
dftmpx1 = dftmp1[dftmp1['ISIN Code'].notna()]
print(dftmpx1.columns)
dftmpx2 = dftmp2[dftmp2['ISIN Code'].notna()]
#dftmpx1 = dftmp1[dftmp1[nav].notna()]
#dftmpx2 = dftmp2[dftmp2[nav].notna()]
dftmp = pd.merge(dftmpx1, dftmpx2, how = 'outer', on = ['Stock_Name', 'INDUSTRY', 'ISIN Code'])
dftmp = dftmp.replace(np.nan, 0)
dftmp['change'] = dftmp[[qty1, qty2]].apply(lambda x: f.find_change(x[0],x[1]), axis=1)
dftmp['change_val'] = dftmp[[qty1, qty2]].apply(lambda x: f.find_act_change(x[0],x[1]), axis=1)
dftmp.to_excel(writer, sheet_name=name)
writer.save()
except Exception:
logger.debug("Unexpected Error")
import functions
import numpy as np
from language import max_letters, language_tags
import pandas as pd
import config
word_data = []
language_data = []
master_dic = []
count = 0
#this function is used to generate dictionary for words in german and english language, and produces a final csv
#which contains each word transformed into a vector and its label.
for tag in config.language_tags.keys():
print('generating dictionary for ' + tag)
dic = functions.create_df(tag, max_letters)
for word in dic:
master_dic.append(word)
vct = functions.convert_dic_to_vector(dic, max_letters)
for vector in vct:
word_data.append(vector)
output_vct = functions.create_output_vector(count, len(language_tags))
for i in range(len(vct)):
language_data.append(output_vct)
count += 1
arr = []
for i in range(len(word_data)):
entry = []
entry.append(master_dic[i])
for digit in language_data[i]:
# Power point check
pp_check = True
while pp_check:
input_pp = int(input("[3/3] Power points:"))
req_pp = int(main_level.get(input_level).get("points")) - input_pp
if req_pp < 0:
print("Are you sure?")
else:
pp_check = False
# Mapping the user input to return correct requirements
req_pp = int(main_level.get(input_level).get("points")) - input_pp
req_coin = main_level.get(input_level).get("coins")
# Store the user input data into database
db = create_df()
if check_brawler(db, input_brawler):
db = update_brawler(db, input_brawler, input_level, req_pp,
req_coin)
else:
db = add_brawler(db, input_brawler, input_level, req_pp, req_coin)
db = order_database(db)
save_df(db)
# Output stage to the user
print("\n{}".format(db))
print("\nMake change to another Brawler?")
make_change_input = input("[y/n]:")
import sys
from functions import give_one, create_df
from data_processing.data_proc import give_three
from funs import give_two
if __name__ == '__main__':
df = create_df()
df['one'] = give_one()
df['two'] = give_two()
df['three'] = give_three()
if df.shape[1] == 4:
print('Main run succesful')
else:
print('Error')
sys.exit(1)
def test_create_df():
df = f.create_df()
if df is not None:
assert True
else:
assert False