def account_depreciation(customer_history, row):
raw_date = row['datetime']
year = metrics.encode_year(raw_date)
price = row['msrp']
price = calculate_real_price(price, year, row)
new_row = row.copy()
new_row.msrp = price
customer_history = customer_history.append(new_row, ignore_index=True)
return customer_history
def uniform_decay(customer):
prices = make_array()
for row in customer.customer_history.iterrows():
date = metrics.encode_year(row['datetime'].values[0])
decay_length = (date - 2006)*2.7+52
for price in row['msrp'].values[0]:
per_year = (price/decay_length)*12 #annual "payment"
for i in np.arange(decay_length/12)
prices.append(per_year)
df = pd.DataFrame({'prices':prices})
return df
def lease_conversion_old(customer):
"""Converts car MSRP to total amount paid by a lease over an average of 36 months."""
leases = 0
for index, row in customer.customer_history.iterrows():
date = metrics.encode_year(row['datetime'])
price = row['msrp']
contract = row['contract_type']
if contract == 'Lease':
depreciation = (price) / 36
interest = (price) * 0.00109
monthly_pay = depreciation + interest
leases += monthly_pay * 36
return leases
def lease_conversion(customer_history, row):
raw_date = row['datetime']
year = metrics.encode_year(raw_date)
price = row['msrp']
price = calculate_real_price(price, year, row)
residual = price * (
1 - metrics.Customer.depreciation_mapping[row['model_class']])
depreciation = price - residual
total_lease = (price + residual) * money_factor * 36 + \
depreciation
new_row = row.copy()
new_row.msrp = total_lease
customer_history = customer_history.append(new_row, ignore_index=True)
return customer_history
def uniform_distribution(customer_history, row):
raw_date = row['datetime']
year = metrics.encode_year(raw_date)
decay_length = mercedes_coeff * ((year - 2006) * 2.7 + 52)
price = row['msrp']
price = calculate_real_price(price, year, row)
num_years = decay_length // 12
per_year = price / num_years # annual "payment"
for i in range(int(num_years)):
new_row = row.copy()
dummy_year = year + i
dummy_year = modify_year(raw_date, dummy_year)
new_row.datetime = dummy_year
new_row.msrp = per_year
customer_history = customer_history.append(new_row, ignore_index=True)
return customer_history