Python for loop to modify DataFrame

Question

How do I use the for loop to execute the following operation?

df_1=pd.concat([df_ab1,df_xy1], axis=1)  

df_2=pd.concat([df_ab2,df_xy2], axis=1)    

df_3=pd.concat([df_ab3,df_xy3], axis=1)   

df_4=pd.concat([df_ab4,df_xy4], axis=1)    

df_5=pd.concat([df_ab5,df_xy5], axis=1)  

df_6=pd.concat([df_ab6,df_xy6], axis=1)    

df_7=pd.concat([df_ab7,df_xy7], axis=1)  

df_8=pd.concat([df_ab8,df_xy8], axis=1)  

df_9=pd.concat([df_ab9,df_xy9], axis=1)  

I am thinking something like this

for i in np.arange(9):  

    df_str(i+1)=pd.concat([df_ab+str(i+1),df_xy+str(i+1)], axis=1)  

Am I missing something?

Answer 1

The solution provided below will solve your problem:

variables = locals()

for i in np.arange(9):  

    variables["df_{0}".format(i+1)] = pd.concat([variables["df_ab{0}".format(i+1)],variables["df_xy{0}".format(i+1)]], axis=1) 

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