Python 尝试实现基于datetime月份的两行数据帧之间的基本减法功能

我试图找出dataframe中股票收盘价和开盘价之间的月平均价差。
基本函数如下所示

for i in range(1,len(df)):
    #difference between today's opening price minus yesterday's closing price
    gap+=(df['Open'][i]-df['Close'][i-1])

Date        Open    High    Low      Close
2019-01-01  10881.7 10923.6 10807.1 10910.1
2019-01-02  10868.8 10895.3 10735.0 10792.5
2019-01-03  10796.8 10814.0 10661.2 10672.2
2019-01-04  10699.7 10741.0 10628.6 10727.3
2019-01-07  10804.8 10835.9 10750.1 10771.8
2019-01-08  10786.2 10818.4 10733.2 10802.1

但是我想在我的数据框中找到每个月的

avg_gap

。并将

avg_gaps

存储在列表中。
Dataframe具有每天的开盘价、最高价、最低价和收盘价以及相关日期（日期时间格式）
看起来像这样

for i in range(1,len(df)):
    #difference between today's opening price minus yesterday's closing price
    gap+=(df['Open'][i]-df['Close'][i-1])

Date        Open    High    Low      Close
2019-01-01  10881.7 10923.6 10807.1 10910.1
2019-01-02  10868.8 10895.3 10735.0 10792.5
2019-01-03  10796.8 10814.0 10661.2 10672.2
2019-01-04  10699.7 10741.0 10628.6 10727.3
2019-01-07  10804.8 10835.9 10750.1 10771.8
2019-01-08  10786.2 10818.4 10733.2 10802.1

像这样，dataframe拥有1.5年的数据

这就是我尝试过的

list=[]
gap=0
count=0

for i in range(1,len(df)):   
    if(df['Date'][i].month==df['Date'][i-1].month):
        count+=1
        gap+=(df['Open'][i]-df['Close'][i-1])
    if(df['Date'][i].month<df['Date'][i-1].month):
        avg_gap=gap/count
        list.append(avg_gap)
        count=0
        gap=0

list=[]
差距=0
计数=0
对于范围（1，len（df））中的i：
如果（df['Date'][i].month==df['Date'][i-1].month）：
计数+=1
间隙+=（df['Open'][i]-df['Close'][i-1]）
如果（df['Date'][i].month您需要在
DataFrame
中创建一个month列来分组。然后，编写一个计算平均差距的函数

间隙计算
您将许多差异加在一起，如下所示：

 df['Open'][1]-df['Close'][0]
+df['Open'][2]-df['Close'][1]
+df['Open'][3]-df['Close'][2]
+...

 (df['Open'][1]+df['Open'][2]+df['Open'][3]+...)
-(df['Close'][0]+df['Close'][1]+df['Close'][2]+...)

但是，我们可以采用不同的方式将其分组如下：

 df['Open'][1]-df['Close'][0]
+df['Open'][2]-df['Close'][1]
+df['Open'][3]-df['Close'][2]
+...

 (df['Open'][1]+df['Open'][2]+df['Open'][3]+...)
-(df['Close'][0]+df['Close'][1]+df['Close'][2]+...)

因此，执行计算的简单方法如下

df['Open'].iloc[1:].sum（）-df['Close'].iloc[:-1].sum（）

代码
很有魅力！非常感谢。