选择列表内数据框中的行，然后附加到另一个列表中的另一个数据框

Question

我有一个包含 n 个数据框的列表中的每日股票数据（每只股票都有自己的数据框）。我想从每个数据帧中以相等的时间间隔选择 m 行并将它们附加到另一个列表中的数据帧。基本上，新列表应该有 m 个数据框 - 这是天数，每个数据框长度 n - 股票数量。 我尝试使用嵌套的 for 循环，但它不起作用

cross_section = []
cross_sections_list = []

for m in range(0, len(datalist[0]), 100):    
    for n in range(len(datalist)):
        cross_section.append(datalist[n].iloc[m])
        cross_sections_list.append(cross_section)

这段代码没有做任何事情。我的机器只是堆在上面。如果有其他方法，例如多索引，我也很想尝试。

例如

输入：

[
             Adj Close   Ticker  
 Date                           
 2020-06-01  321.850006   AAPL  
 2020-06-02  323.339996   AAPL  
 2020-06-03  325.119995   AAPL  
 2020-06-04  322.320007   AAPL  
 2020-06-05  331.500000   AAPL  
 2020-06-08  333.459991   AAPL  
 2020-06-09  343.989990   AAPL  
 2020-06-10  352.839996   AAPL  ,

             Adj Close    Ticker  
 Date                           
 2020-06-01  182.830002   MSFT  
 2020-06-02  184.910004   MSFT  
 2020-06-03  185.360001   MSFT  
 2020-06-04  182.919998   MSFT  
 2020-06-05  187.199997   MSFT  
 2020-06-08  188.360001   MSFT  
 2020-06-09  189.800003   MSFT  
 2020-06-10  196.839996   MSFT  ]

输出：

 [
             Adj Close   Ticker  
 Date                           
 2020-06-01  321.850006   AAPL  
 2020-06-01  182.830002   MSFT  ,

             Adj Close   Ticker  
 Date                           
 2020-06-03  325.119995   AAPL  
 2020-06-03  185.360001   MSFT  ,

             Adj Close   Ticker  
 Date                           
 2020-06-05  331.500000   AAPL  
 2020-06-05  187.199997   MSFT  ]

等等。

谢谢

Answer 1

data_global = pd.DataFrame()
for i in datalist:
    data_global = data_global.append(i) #first merge all dataframes into one

data_by_date = [i for x, i in data_global.groupby('Date')] #step 2: group the data by date

each_nth_day = []
for i in range(0, len(data_by_date), 21):
    each_nth_day.append(data_by_date[i]) #lastly take each n-th dataframe (21 in this case)

感谢您的帮助 user13802115

Answer 2

不完全清楚你想要什么，但这里有一些代码希望能有所帮助。

list_of_df = [] #list of all the df's

alldf = pd.concat(list_of_df) #brings all df's into one df

list_of_grouped = [y for x, y in alldf.groupby('Date')] #separates df's by date and puts them in a list

number_of_days = alldf.groupby('Date').ngroups #Total number of groups (Dates)

list_of_Dates = [x for x, y in alldf.groupby('Date')] #List of all the dates that were grouped

count_of_stocks = []
for i in range(len(list_of_grouped)):
    count_of_stocks.append(len(list_of_grouped[i])) #puts count of each grouped df into a list

zipped = list(zip(list_of_data,count_of_stocks)) #combines the dates and count of stocks in a list to see how many stocks are in each date.