def print_spender_data():
"""Reports out the current spending data."""
print('\nSPENDER DATA\n==============')
print('Total spending: ${:,.2f} across {} transactions'.format(
Spender.get_total_spending(), Spender.get_num_transactions()))
print('Average spending across {} spenders: ${:,.2f}'.format(
Spender.get_num_spenders(), Spender.get_avg_spending()),
end='\n')
for name in spenders.keys():
print('')
print(spenders[name])
def print_payback_instructions():
"""The below prints out instructions for all Spender members who were
added to the 'owers' set of names - those with negative deviations from
the average spending. This function iterates through each Spender member
whose name was added to the owers list, gets their deviation, and
divides it by the number of Spender members whose names were added to
the 'owed' set of names. Thus, each ower is to pay an equal amount of
what they owe to each Spender member who is owed. The conditions in the
function govern proper syntax depending on how many are in each list."""
num_owed = len(Spender.get_owed_spenders())
print('\nPAYBACK INSTRUCTIONS\n======================')
if num_owed > 0:
for ower_name in Spender.get_owing_spenders():
print(ower_name, end=': pay ')
for index, owed_name in enumerate(Spender.get_owed_spenders()):
pay_to_each = (
abs(spenders[ower_name].get_spender_deviation()) /
num_owed)
if num_owed == 1:
print(owed_name + ' ${:,.2f}'.format(pay_to_each))
elif num_owed == 2:
if not index == num_owed - 1:
print(owed_name, end=' ')
else:
print('and ' + owed_name +
' ${:,.2f} each'.format(pay_to_each))
elif num_owed > 2:
if not index == num_owed - 1:
print(owed_name, end=', ')
else:
print('and ' + owed_name +
' ${:,.2f} each'.format(pay_to_each))
else:
print('Nobody owes anyone anything! Even Steven!')
def update_spender_records(expenses_df):
"""Each time this runs, it has been passed a new dataframe of expenses
to add to the spenders' records. It iterates over the new spending
records, looking for Spender names and expenses. When it finds a record
with a new spender name, it instantiates them as a new member of the
Spender class, and saves them into the dictionary of spenders by their
name as a key, with the corresponding first found transaction as the
initializing balance. For any records for existing spenders, it calls
an increment method to add the found value for the transaction to the
corresponding spender object's balance attribute. This is implemented
via pandas, which handles the excel spreadsheet as an iterable
dataframe."""
for index, row in expenses_df.iterrows():
name = row['Spender']
amount = row['Amount']
# check the keylist for spender name. If found name not already
# in dictionary, instantiate new Spender member with starting
# balance of their first transaction
# also, track number of transactions found for recordkeeping
if name not in spenders.keys():
spenders.update({name: Spender(name, s_bal=amount)})
Spender.increment_num_spenders()
Spender.increment_num_transactions()
# if the name in this row's spender column is already there, process
# that line's data instead, and increment that Spender's balance
# also, count number of transactions for recordkeeping
elif name in spenders.keys():
Spender.increment_spender_balance(spenders[name], amount)
Spender.increment_num_transactions()
# finally, add the amount found on this line to the total spending
# for all spenders in the class-level attribute total_spending
Spender.increment_total_spending(amount)
# get average spending (total transactions / number of spenders)
Spender.set_avg_spending()
# in case run before, reset the names of who owes and who is owed
# before re-populating based on the latest spending data
Spender.clear_owed_spenders()
Spender.clear_owing_spenders()
# iterate through each Spender member, and set its deviation (distance
# from the average). If positive deviation, they are owed that money;
# if negative, they owe that money to others.
for name in spenders.keys():
spenders[name].set_spender_deviation()
# if deviation positive, add to set of 'owed' names
if spenders[name].get_spender_deviation() > 0:
Spender.add_owed_spender(name)
# if negative, add to set of 'owers' names
elif spenders[name].get_spender_deviation() < 0:
Spender.add_owing_spender(name)
# badvalues_df = pd.read_excel(file)
#
# assert validate_data(badvalues_df) is ValueError, 'Erroneously allowing non-floats'
file = 'test_record.xlsx'
df = pd.read_excel(file)
assert not validate_data(df) is False, 'Erroneously disallowing good data'
try:
update_spender_records(df)
except NameError:
raise AssertionError('Dataframe not found.')
# total in spreadsheet is 100 + 10 + 100 + 10 + 80 = 300.0; test for that
assert Spender.get_total_spending() == 300.0
# test that class is reading names from file correctly
assert spenders['Ladybug'].get_spender_name() == 'Ladybug'
# test member balances:
assert spenders['Ladybug'].get_spender_balance() == 20.0
assert spenders['Parrot'].get_spender_balance() == 200.0
assert spenders['Octopus'].get_spender_balance() == 80.0
# test number of spenders
assert Spender.get_num_spenders() == 3
# test number of transactions
assert Spender.get_num_transactions() == 5