遍历并合并具有相同索引，相同列的DataFrame（但是每个DataFrame唯一的几列）

Question

所需任务说明

我使用以下代码合并df和df1（显示示例数据），并且它可以很好地满足我的需求。但是，我需要循环访问大量的DataFrame（例如df2，但将是df3，df4等），并且不确定如何修改代码。我的DataFrame具有相同的索引，相同的列，但是每个DataFrame都有一些单独的列。我使用下面的代码，并且效果很好，但是我想对其进行修改，以便可以遍历df和df1，将它们合并在一起，创建requireddata，然后在requireddata与[C0合并在一起时重复此步骤]。 df2与requireddata合并，将继续相同的逻辑，依此类推。任何帮助都是极好的！！ ：）

df3

df

       ID    AA  TA  TL
Date                      
2001  AAPL   1.0  44  50 
2002  AAPL   3.0  33  51 
2003  AAPL   2.0  22  53 
2004  AAPL   5.0  11  76 
2005  AAPL   2.0  33  44 
2006  AAPL   3.0  22  12 

df1

       ID    AA  TA  ML
Date                      
2001  MSFT   3.5  44  12
2002  MSFT   6.7  33  15
2003  MSFT   2.3  22  19
2004  MSFT   5.5  11  20
2005  MSFT   2.2  33  43
2006  MSFT   3.2  22  23

示例

df2

使用的代码

       ID    AA  TA  PP
Date                      
2001  TSLA   3.3  48  18
2002  TSLA   6.3  38  18
2003  TSLA   2.6  28  18
2004  TSLA   5.3  18  28
2005  TSLA   2.3  38  48
2006  TSLA   3.3  28  28

创建此：

dfdates['Date'] # this has dates required for index
df
df1

cols_to_use = df.columns.difference(df1.columns) #compare column difference df and df1
cols_to_use1 = df1.columns.difference(df.columns) #compare column difference df1 and df

dataframe = pd.DataFrame(columns = cols_to_use, index = df['Date']) #dataframe with columns in df1 but not in df
dataframe1 = pd.DataFrame(columns = cols_to_use1, index = df1['Date']) #dataframe with columns in df but not in df1

datatesting = pd.concat([dataframe, df], axis=1) #merge missing columns into df
datatesting1 = pd.concat([dataframe1, df1], axis=1) #merge missing columns into df1

diff = datatesting1.columns.difference(datatesting.columns) #check difference (is 0)
print (diff)
frames = [datatesting, datatesting1] #list of dataframes 
requireddata = pd.concat(frames) #merge dataframes

具有循环代码，喜欢这样的东西：

       ID    AA   TA   TL  ML
Date                      
2001  AAPL   1.0  44  50  NaN
2002  AAPL   3.0  33  51  NaN
2003  AAPL   2.0  22  53  NaN
2004  AAPL   5.0  11  76  NaN
2005  AAPL   2.0  33  44  NaN
2006  AAPL   3.0  22  12  NaN                    
2001  MSFT   3.5  44  NaN  12
2002  MSFT   6.7  33  NaN  15
2003  MSFT   2.3  22  NaN  19
2004  MSFT   5.5  11  NaN  20
2005  MSFT   2.2  33  NaN  43
2006  MSFT   3.2  22  NaN  23

Answer 1

我相信这里没有必要区别列，仅使用

       ID    AA   TA   TL  ML  PP
Date                      
2001  AAPL   1.0  44  50  NaN  NaN
2002  AAPL   3.0  33  51  NaN  NaN
2003  AAPL   2.0  22  53  NaN  NaN
2004  AAPL   5.0  11  76  NaN  NaN
2005  AAPL   2.0  33  44  NaN  NaN
2006  AAPL   3.0  22  12  NaN  NaN                  
2001  MSFT   3.5  44  NaN  12  NaN
2002  MSFT   6.7  33  NaN  15  NaN
2003  MSFT   2.3  22  NaN  19  NaN
2004  MSFT   5.5  11  NaN  20  NaN
2005  MSFT   2.2  33  NaN  43  NaN
2006  MSFT   3.2  22  NaN  23  NaN
2001  TSLA   3.3  48  NaN  NaN  18
2002  TSLA   6.3  38  NaN  NaN  18
2003  TSLA   2.6  28  NaN  NaN  18
2004  TSLA   5.3  18  NaN  NaN  28
2005  TSLA   2.3  38  NaN  NaN  48
2006  TSLA   3.3  28  NaN  NaN  28

，列正确对齐：

concat