Suppose I have the following code that calculates how many products I can purchase given my budget-
import math
import pandas as pd
data = [['2021-01-02', 5.5], ['2021-02-02', 10.5], ['2021-03-02', 15.0], ['2021-04-02', 20.0]]
df = pd.DataFrame(data, columns=['Date', 'Current_Price'])
df.Date = pd.to_datetime(df.Date)
mn = df.Date.min()
mx = df.Date.max()
dr = pd.date_range(mn - pd.tseries.offsets.MonthBegin(), mx + pd.tseries.offsets.MonthEnd(), name="Date")
df = df.set_index("Date").reindex(dr).reset_index()
df['Current_Price'] = df.groupby(
pd.Grouper(key='Date', freq='1M'))['Current_Price'].ffill().bfill()
# The dataframe below shows the current price of the product
# I'd like to buy at the specific date_range
print(df)
# Create 'Day' column to know which day of the month
df['Day'] = pd.to_datetime(df['Date']).dt.day
# Create 'Deposit' column to record how much money is
# deposited in, say, my bank account to buy the product.
# 'Withdrawal' column is to record how much I spent in
# buying product(s) at the current price on a specific date.
# 'Num_of_Products_Bought' shows how many items I bought
# on that specific date.
#
# Please note that the calculate below takes into account
# the left over money, which remains after I've purchased a
# product, for future purchase. For example, if you observe
# the resulting dataframe at the end of this code, you'll
# notice that I was able to purchase 7 products on March 1, 2021
# although my deposit on that day was $100. That is because
# on the days leading up to March 1, 2021, I have been saving
# the spare change from previous product purchases and that
# extra money allows me to buy an extra product on March 1, 2021
# despite my budget of $100 should only allow me to purchase
# 6 products.
df[['Deposit', 'Withdrawal', 'Num_of_Products_Bought']] = 0.0
# Suppose I save $100 at the beginning of every month in my bank account
df.loc[df['Day'] == 1, 'Deposit'] = 100.0
for index, row in df.iterrows():
if df.loc[index, 'Day'] == 1:
# num_prod_bought = (sum_of_deposit_so_far - sum_of_withdrawal)/current_price
df.loc[index, 'Num_of_Products_Bought'] = math.floor(
(sum(df.iloc[0:(index + 1)]['Deposit'])
- sum(df.iloc[0:(index + 1)]['Withdrawal']))
/ df.loc[index, 'Current_Price'])
# Record how much I spent buying the product on specific date
df.loc[index, 'Withdrawal'] = df.loc[index, 'Num_of_Products_Bought'] * df.loc[index, 'Current_Price']
print(df)
# This code above is working as intended,
# but how can I make it more efficient/pandas-like?
# In particular, I don't like to idea of having to
# iterate the rows and having to recalculate
# the running (sum of) deposit amount and
# the running (sum of) the withdrawal.
As mentioned in the comment in the code, I would like to know how to accomplish the same without having to iterate the rows one by one and calculating the sum of the rows up to the current row in my iteration (I read around StackOverflow and saw cumsum() function, but I don't think cumsum has the notion of current row in the iteration).
Thank you very much in advance for your suggestions/answers!
A solution using .apply:
def fn():
leftover = 0
amount, deposit = yield
while True:
new_amount, new_deposit = yield (deposit + leftover) // amount
leftover = (deposit + leftover) % amount
amount, deposit = new_amount, new_deposit
df = df.set_index("Date")
s = fn()
next(s)
m = df.index.day == 1
df.loc[m, "Deposit"] = 100
df.loc[m, "Num_of_Products_Bought"] = df.loc[
m, ["Current_Price", "Deposit"]
].apply(lambda x: s.send((x["Current_Price"], x["Deposit"])), axis=1)
df.loc[m, "Withdrawal"] = (
df.loc[m, "Num_of_Products_Bought"] * df.loc[m, "Current_Price"]
)
print(df.fillna(0).reset_index())
Prints:
Date Current_Price Deposit Num_of_Products_Bought Withdrawal
0 2021-01-01 5.5 100.0 18.0 99.0
1 2021-01-02 5.5 0.0 0.0 0.0
2 2021-01-03 5.5 0.0 0.0 0.0
3 2021-01-04 5.5 0.0 0.0 0.0
4 2021-01-05 5.5 0.0 0.0 0.0
5 2021-01-06 5.5 0.0 0.0 0.0
6 2021-01-07 5.5 0.0 0.0 0.0
7 2021-01-08 5.5 0.0 0.0 0.0
8 2021-01-09 5.5 0.0 0.0 0.0
9 2021-01-10 5.5 0.0 0.0 0.0
10 2021-01-11 5.5 0.0 0.0 0.0
11 2021-01-12 5.5 0.0 0.0 0.0
12 2021-01-13 5.5 0.0 0.0 0.0
13 2021-01-14 5.5 0.0 0.0 0.0
14 2021-01-15 5.5 0.0 0.0 0.0
15 2021-01-16 5.5 0.0 0.0 0.0
16 2021-01-17 5.5 0.0 0.0 0.0
17 2021-01-18 5.5 0.0 0.0 0.0
18 2021-01-19 5.5 0.0 0.0 0.0
19 2021-01-20 5.5 0.0 0.0 0.0
20 2021-01-21 5.5 0.0 0.0 0.0
21 2021-01-22 5.5 0.0 0.0 0.0
22 2021-01-23 5.5 0.0 0.0 0.0
23 2021-01-24 5.5 0.0 0.0 0.0
24 2021-01-25 5.5 0.0 0.0 0.0
25 2021-01-26 5.5 0.0 0.0 0.0
26 2021-01-27 5.5 0.0 0.0 0.0
27 2021-01-28 5.5 0.0 0.0 0.0
28 2021-01-29 5.5 0.0 0.0 0.0
29 2021-01-30 5.5 0.0 0.0 0.0
30 2021-01-31 5.5 0.0 0.0 0.0
31 2021-02-01 10.5 100.0 9.0 94.5
32 2021-02-02 10.5 0.0 0.0 0.0
33 2021-02-03 10.5 0.0 0.0 0.0
34 2021-02-04 10.5 0.0 0.0 0.0
35 2021-02-05 10.5 0.0 0.0 0.0
36 2021-02-06 10.5 0.0 0.0 0.0
37 2021-02-07 10.5 0.0 0.0 0.0
38 2021-02-08 10.5 0.0 0.0 0.0
39 2021-02-09 10.5 0.0 0.0 0.0
40 2021-02-10 10.5 0.0 0.0 0.0
41 2021-02-11 10.5 0.0 0.0 0.0
42 2021-02-12 10.5 0.0 0.0 0.0
43 2021-02-13 10.5 0.0 0.0 0.0
44 2021-02-14 10.5 0.0 0.0 0.0
45 2021-02-15 10.5 0.0 0.0 0.0
46 2021-02-16 10.5 0.0 0.0 0.0
47 2021-02-17 10.5 0.0 0.0 0.0
48 2021-02-18 10.5 0.0 0.0 0.0
49 2021-02-19 10.5 0.0 0.0 0.0
50 2021-02-20 10.5 0.0 0.0 0.0
51 2021-02-21 10.5 0.0 0.0 0.0
52 2021-02-22 10.5 0.0 0.0 0.0
53 2021-02-23 10.5 0.0 0.0 0.0
54 2021-02-24 10.5 0.0 0.0 0.0
55 2021-02-25 10.5 0.0 0.0 0.0
56 2021-02-26 10.5 0.0 0.0 0.0
57 2021-02-27 10.5 0.0 0.0 0.0
58 2021-02-28 10.5 0.0 0.0 0.0
59 2021-03-01 15.0 100.0 7.0 105.0
60 2021-03-02 15.0 0.0 0.0 0.0
61 2021-03-03 15.0 0.0 0.0 0.0
62 2021-03-04 15.0 0.0 0.0 0.0
63 2021-03-05 15.0 0.0 0.0 0.0
64 2021-03-06 15.0 0.0 0.0 0.0
65 2021-03-07 15.0 0.0 0.0 0.0
66 2021-03-08 15.0 0.0 0.0 0.0
67 2021-03-09 15.0 0.0 0.0 0.0
68 2021-03-10 15.0 0.0 0.0 0.0
69 2021-03-11 15.0 0.0 0.0 0.0
70 2021-03-12 15.0 0.0 0.0 0.0
71 2021-03-13 15.0 0.0 0.0 0.0
72 2021-03-14 15.0 0.0 0.0 0.0
73 2021-03-15 15.0 0.0 0.0 0.0
74 2021-03-16 15.0 0.0 0.0 0.0
75 2021-03-17 15.0 0.0 0.0 0.0
76 2021-03-18 15.0 0.0 0.0 0.0
77 2021-03-19 15.0 0.0 0.0 0.0
78 2021-03-20 15.0 0.0 0.0 0.0
79 2021-03-21 15.0 0.0 0.0 0.0
80 2021-03-22 15.0 0.0 0.0 0.0
81 2021-03-23 15.0 0.0 0.0 0.0
82 2021-03-24 15.0 0.0 0.0 0.0
83 2021-03-25 15.0 0.0 0.0 0.0
84 2021-03-26 15.0 0.0 0.0 0.0
85 2021-03-27 15.0 0.0 0.0 0.0
86 2021-03-28 15.0 0.0 0.0 0.0
87 2021-03-29 15.0 0.0 0.0 0.0
88 2021-03-30 15.0 0.0 0.0 0.0
89 2021-03-31 15.0 0.0 0.0 0.0
90 2021-04-01 20.0 100.0 5.0 100.0
91 2021-04-02 20.0 0.0 0.0 0.0
92 2021-04-03 20.0 0.0 0.0 0.0
93 2021-04-04 20.0 0.0 0.0 0.0
94 2021-04-05 20.0 0.0 0.0 0.0
95 2021-04-06 20.0 0.0 0.0 0.0
96 2021-04-07 20.0 0.0 0.0 0.0
97 2021-04-08 20.0 0.0 0.0 0.0
98 2021-04-09 20.0 0.0 0.0 0.0
99 2021-04-10 20.0 0.0 0.0 0.0
100 2021-04-11 20.0 0.0 0.0 0.0
101 2021-04-12 20.0 0.0 0.0 0.0
102 2021-04-13 20.0 0.0 0.0 0.0
103 2021-04-14 20.0 0.0 0.0 0.0
104 2021-04-15 20.0 0.0 0.0 0.0
105 2021-04-16 20.0 0.0 0.0 0.0
106 2021-04-17 20.0 0.0 0.0 0.0
107 2021-04-18 20.0 0.0 0.0 0.0
108 2021-04-19 20.0 0.0 0.0 0.0
109 2021-04-20 20.0 0.0 0.0 0.0
110 2021-04-21 20.0 0.0 0.0 0.0
111 2021-04-22 20.0 0.0 0.0 0.0
112 2021-04-23 20.0 0.0 0.0 0.0
113 2021-04-24 20.0 0.0 0.0 0.0
114 2021-04-25 20.0 0.0 0.0 0.0
115 2021-04-26 20.0 0.0 0.0 0.0
116 2021-04-27 20.0 0.0 0.0 0.0
117 2021-04-28 20.0 0.0 0.0 0.0
118 2021-04-29 20.0 0.0 0.0 0.0
119 2021-04-30 20.0 0.0 0.0 0.0