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? Select the correct answer from above options

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) Learn data science with python course to improve your technical knowledge.