将Date转换为日期时间，并将年份与其他列进行聚合，以计算总和：

df['Date'] = pd.to_datetime(df['Date'])
df1 = (df.groupby([df['Date'].dt.year.rename('Year'), 'Area','Incidents']).Speed
         .sum() 
         .reset_index())

可以使用masks选择所需的数据，然后取平均值：

import pandas as pd
data = {'Date' : ['1/1/2016', '1/2/2016', '1/3/2016', '1/1/2017', '1/12/2017', '4/8/2017', '3/12/2017'],
        'Speed' : [6.5, 7.7, 14, 5.5, 9.8, 3, 5.8],
        'Area' : ['Maly', 'Maly', 'Maly', 'Reet', 'Reet', 'Reet', 'Anlow'],
        'Incidents':['L', 'H', 'H', 'M', 'M', 'H', 'L']}
df = pd.DataFrame(data)

# Convert the 'date' to datetime and set it as the index

df['Date'] = pd.to_datetime(df['Date'])
df.index = df['Date']

# Create the mask(s) you want and take the mean of the chosen column

mean_wind_speed = df[(df.index.year==2017) & (df.Area=='Reet') & (df.Incidents == 'M')]['Speed'].mean()
print(mean_wind_speed)

输出：

7.65

编辑

您还可以使用query，其中包含一个新的Years列：

df['Year'] = df.index.year
mean_wind_speed = df.query('Year == 2017 and Area == "Reet" and Incidents=="M"')['Speed'].mean()