Python 在对时间序列数据中的每一行进行迭代时，计算熊猫中最多到当前行的总和
假设我有下面的代码，根据我的预算计算我可以购买多少产品-Python 在对时间序列数据中的每一行进行迭代时，计算熊猫中最多到当前行的总和,python,pandas,cumsum,Python,Pandas,Cumsum,假设我有下面的代码，根据我的预算计算我可以购买多少产品- 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)
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.
cumsum（）cumsum非常感谢您的建议/回答 使用
的解决方案。应用：
def fn（）：
剩余=0
金额，存款=收益率
尽管如此：
新金额，新存款=收益率（存款+剩余）//金额
剩余=（存款+剩余）%金额
金额，存款=新金额，新存款
df=df.set_索引（“日期”）
s=fn（）
下一个(s)
m=df.index.day==1
df.loc[m，“存款”]=100
df.loc[m，“购买的产品数量”]=df.loc[
m、 [“当前价格”、“存款”]
].apply（lambda x:s.send（（x[“当前价格”），x[“存款”），axis=1）
df.loc[m，“撤回”]=(
df.loc[m，“购买的产品数量”]*df.loc[m，“当前价格”]
)
打印（df.fillna（0.reset_index（））
印刷品：
日期当前价格存款购买的产品数量取款
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 2