I'm trying to create a nested dictionary with a set of values that are pulled from a for-loop, to measure growth and revenue amounts for various customer-product pairings. However, when I loop through a dataframe to set elements of the dictionary, each dictionary element ends up with the same values. What's going on, here?
I have already tried changing various elements of how the lists are built, but to no avail.
'''
TP_Name = customer name
Service_Level_1 = service name
100.2014 is just a marker to show that someone has started consuming the service
tpdict is already created with necessary nesting below with empty values at each endpoint
'''
for col in pivotdf.columns:
growthlist = []
amountlist = []
first = True
TP_Name, Service_Level_1 = col.split('___')
for row in pivotdf[col]:
if first == True:
past = row+.00001
first = False
if row == 0 and past <.0001 :
growth = 0
elif row != 0 and past == .00001:
growth = 100.2014
else:
current = row
growth = (current-past)/past
growth = round(growth,4)
growthlist.append(growth)
past = row +.00001
amountlist.append(row)
tpdict[TP_Name][Service_Level_1]['growth'] = growthlist
tpdict[TP_Name][Service_Level_1]['amount'] = amountlist
'''
problem: Each value ends up being the same thing
'''
Expected results:
{'CUSTOMER NAME': {'PRODUCT1': {'growth': [unique_growthlist], 'amount': [unique_amountlist]}, 'PRODUCT2': {'growth': [unique_growthlist],'amount': [unique_amountlist]}}}
A dictionary is a key value pair (as I am sure you may know). If you ever try to write to a dictionary with a key that already exists in the dictionary then the dictionary will overwrite the value for that key.
Example:
d = dict()
d[1] = 'a' # d = {1: 'a'}
d[1] = 'b' # d = {1: 'b'}
Your project seems like it may be a good use of a namedtuple
in python. A namedtuple
is basically a light weight class/object. My example code may be wrong because I don't know how your for
loop is working (commenting helps everyone). That being said here is an example.
I only make this recommendation as dictionaries
consume ~33% more memory then the objects they hold (though they are much faster).
from collections import namedtuple
Customer = namedtuple('Customer', 'name products')
Product = namedtuple('Product', 'growth amount')
customers = []
for col in pivotdf.columns:
products = []
growthlist = []
amountlist = []
first = True
TP_Name, Service_Level_1 = col.split('___')
for row in pivotdf[col]:
if first == True:
past = row + .00001
first = False
if row == 0 and past < .0001 :
growth = 0
elif row != 0 and past == .00001:
growth = 100.2014
else:
current = row
growth = (current - past) / past
growth = round(growth, 4)
growthlist.append(growth)
past = row + .00001
amountlist.append(row)
cur_product = Product(growth=growthlist, amount=amountlist) # Create a new product
products.append(cur_product) # Add that product to our customer
# Create a new customer with our products
cur_customer = Customer(name=TP_Name, products=products)
customers.append(cur_customer) # Add our customer to our list of customers
Here customers
is a list of Customer namedtuples
that we can use as objects. For example this is how we can print them out.
for customer in customers:
print(customer.name, customer.products) # Print each name and their products
for growth, amount in customer.products:
print(growth, amount) # Print growth and amount for each product.
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