Question

我有以下代码：

import csv
import pandas as pd
import numpy as np


stocks_dataframe = pd.read_csv('^GSPC.csv', delimiter = ',')        


stocks_dataframe['Percent_change'] = stocks_dataframe['Close'].pct_change()
stocks_dataframe['positive_return_day'] = np.where(stocks_dataframe['Percent_change']>=0, 1, 0)
stocks_dataframe['negative_return_day'] = np.where(stocks_dataframe['Percent_change']<0, 1, 0)
stocks_dataframe['positive_return_day'].value_counts()
stocks_dataframe['date'] = pd.to_datetime(stocks_dataframe['Date'])
stocks_dataframe['year'], stocks_dataframe['month'] = stocks_dataframe['date'].dt.year, stocks_dataframe['date'].dt.month
yearly_data = pd.DataFrame()
yearly_data['positive_return_day'] = stocks_dataframe['positive_return_day'].groupby([stocks_dataframe.year]).agg('sum')
yearly_data['negative_return_day'] = stocks_dataframe['negative_return_day'].groupby([stocks_dataframe.year]).agg('sum')


stocks_dataframe.groupby(stocks_dataframe.year)['Percent_change'].transform('mean')

如何分别计算正回报日和负回报日的平均回报？我想每年获取这些值，并将它们存储在Annual_data数据框中。

这是股票数据框的头部：

stocks_dataframe.head()
Out[35]: 
         Date         Open         High  ...  year  month  negative_return_day
0  1999-12-31  1464.469971  1472.420044  ...  1999     12                    0
1  2000-01-03  1469.250000  1478.000000  ...  2000      1                    1
2  2000-01-04  1455.219971  1455.219971  ...  2000      1                    1
3  2000-01-05  1399.420044  1413.270020  ...  2000      1                    0
4  2000-01-06  1402.109985  1411.900024  ...  2000      1                    0

[5 rows x 13 columns]

Answer 1

你不能再次分组吗？

for year, df in stocks_dataframe.groupby(stocks_dataframe.year):
     print(year)
     print(df.groupby(df.negative_return_day).Percent_change.mean())

编辑：现在您也可以获得年份

Python数据框-每年的条件平均值

1 个答案: