#Create separate dataframes according to the different selected discounts.
selected_d = np.array([.05,.1,.15,.2,.25])#Array of selected discounts.
#List of dataframes for different discount levels.
dfs=[]
for d in selected_d:
    df_dl = df_orderd[df_orderd['Discount'] == d]
    dfs.append(df_dl)

#Get results for hypothesis testing.
results=[]
for i in range(len(dfs)):
    print(f'Hypothesis testing results for discount level of: {selected_d[i]*100}%')
    welch = welch_t(dfs[1].Quantity,df_ordernd.Quantity)
    d = cohen_d(dfs[i].Quantity,df_ordernd.Quantity)
    decision = 'Reject the Null' if welch[1]<0.05 else 'Fail to Reject the Null'
    result = [f'{selected_d[i]*100}%', decision, d]
    results.append(result)
    print('\n')
results_df=pd.DataFrame(results,columns=['Discount_Level', 'Decision', "Cohen_d"])
results_df.sort_values('Cohen_d')