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Hi I need my dataframe with different names. The dataframe is creating insde a for loop as shown below.

for lambda_ in range(0,len(tuned_parameter)-1):
    print ('....................................',lambda_)
    i=0
    for train_index, test_index in kf.split(X):

        print("Train:", train_index, "Validation:",test_index)
        X_train, X_test = X.iloc[train_index], X.iloc[test_index]
        print ('X train ............',X_train.shape)
        print ('X_test...............',X_test.shape)
        mean_returns_Train = X_train.mean()
        cov_matrix_Train=X_train.cov()
        mean_returns_Test = X_test.mean()
        cov_matrix_Test = X_test.cov()      
        max_s, min_s = display_simulated_ef_with_random(mean_returns_Train, cov_matrix_Train, num_portfolios, risk_free_rate,returns, tuned_parameter[lambda_])
        #print (max_s.T)
        appended_data.append(max_s.T)


    appended_data = pd.concat(appended_data, axis=1)   
    print (appended_data.mean(axis=1))
    i=i+1

But I need my for loop to be like, appended_data0, appended_data1,appended_data3.....

How can I create multiple dataframes where its name ending with the related 'i' value

user3789200
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1 Answers1

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The issue worked with an adjustment like below. As suggested in the comments section, used a dictionary to store the dataframes.

whole_dataframes = {}

#k = int(np.floor(float(X.shape[0]) / number_folds))
weights = np.zeros((3,num_portfolios))
for lambda_ in range(0,len(tuned_parameter)-1):
    print ('....................................',lambda_)
    i=0
    appended_data = []


    for train_index, test_index in kf.split(X):

        print("Train:", train_index, "Validation:",test_index)
        X_train, X_test = X.iloc[train_index], X.iloc[test_index]
        print ('X train ............',X_train.shape)
        print ('X_test...............',X_test.shape)
        mean_returns_Train = X_train.mean()
        cov_matrix_Train=X_train.cov()
        mean_returns_Test = X_test.mean()
        cov_matrix_Test = X_test.cov()      
        max_s, min_s = display_simulated_ef_with_random(mean_returns_Train, cov_matrix_Train, num_portfolios, risk_free_rate,returns, tuned_parameter[lambda_])
        #print (max_s.T)
        appended_data.append(max_s.T)


    appended_data = pd.concat(appended_data, axis=1)      
    whole_dataframes['Variable{}'.format(i)]=appended_data.mean(axis=1)
    print (whole_dataframes['Variable{}'.format(i)])
    i=i+1
user3789200
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