Numpy Savetxt 覆盖，无法找出放置循环的位置

Question

我正在创建一个计算客户数据之间相关性的程序。我想将相关值打印到 CSV，以便进一步分析数据。

我已经成功地让我的程序遍历所有客户（每个客户 12 个月的数据），同时计算他们在多个安排中的个人相关性。如果我打印到对话框，我可以看到这一点。

但是，当我尝试使用 Savetxt 保存时，我只得到了我计算的最终值。

我认为我将 for 循环放在了错误的位置，它应该放在哪里？我已经尝试查看其他问题，但并没有对它提供太多启示。

编辑：我已尝试按照建议将写作与外部 for 循环和内部 for 循环对齐，两者都产生了相同的结果。

for x_customer in range(0,len(overalldata),12):

        for x in range(0,13,1):
                cust_months = overalldata[0:x,1]
                cust_balancenormal = overalldata[0:x,16]
                cust_demo_one = overalldata[0:x,2]
                cust_demo_two = overalldata[0:x,3]
                num_acct_A = overalldata[0:x,4]
                num_acct_B = overalldata[0:x,5]
    #Correlation Calculations
                demo_one_corr_balance = numpy.corrcoef(cust_balancenormal, cust_demo_one)[1,0]
                demo_two_corr_balance = numpy.corrcoef(cust_balancenormal, cust_demo_two)[1,0]
                demo_one_corr_acct_a = numpy.corrcoef(num_acct_A, cust_demo_one)[1,0]
                demo_one_corr_acct_b = numpy.corrcoef(num_acct_B, cust_demo_one)[1,0]
                demo_two_corr_acct_a = numpy.corrcoef(num_acct_A, cust_demo_two)[1,0]
                demo_two_corr_acct_b = numpy.corrcoef(num_acct_B, cust_demo_two)[1,0]

                result_correlation = [(demo_one_corr_balance),(demo_two_corr_balance),(demo_one_corr_acct_a),(demo_one_corr_acct_b),(demo_two_corr_acct_a),(demo_two_corr_acct_b)]

        result_correlation_combined = emptylist.append([result_correlation])
        cust_delete_list = [0,(x_customer),1]
        overalldata = numpy.delete(overalldata, (cust_delete_list), axis=0)

numpy.savetxt('correlationoutput.csv', numpy.column_stack(result_correlation), delimiter=',')
print result_correlation

Answer 1

这部分代码只是草率：

                result_correlation = [(demo_one_corr_balance),...]

        result_correlation_combined = emptylist.append([result_correlation])
        cust_delete_list = [0,(x_customer),1]
        overalldata = numpy.delete(overalldata, (cust_delete_list), axis=0)

numpy.savetxt('correlationoutput.csv', numpy.column_stack(result_correlation), delimiter=',')
print result_correlation

您在最里面的循环中设置result_correlation，然后在最后的保存和打印中使用它。显然它会打印最后一个循环的结果。

同时，您将它附加到result_correlation_combined，在x 循环之外，靠近x_customer 循环的趋势。但是您不会对列表做任何事情。

最后在x_customer 循环中，您可以使用overalldata，但我看不到任何进一步的用途。

暂时忘记savetxt，直接收集数据。

Answer 2

我接受了上述海报的建议并更正了我的代码。我现在可以写入文件了。但是，我对完成的迭代次数有疑问，我将在另一个问题中发布它，因为它不相关。这是我使用的解决方案。

for x_customer in range(0,len(overalldata),12):

        for x in range(0,13,1):
                cust_months = overalldata[0:x,1]

                cust_balancenormal = overalldata[0:x,16]

                cust_demo_one = overalldata[0:x,2]
                cust_demo_two = overalldata[0:x,3]

                num_acct_A = overalldata[0:x,4]
                num_acct_B = overalldata[0:x,5]

                out_mark_channel_one = overalldata[0:x,25]
                out_service_channel_two = overalldata[0:x,26]
                out_mark_channel_three = overalldata[0:x,27]
                out_mark_channel_four = overalldata[0:x,28]


    #Correlation Calculations

                #Demographic to Balance Correlations
                demo_one_corr_balance = numpy.corrcoef(cust_balancenormal, cust_demo_one)[1,0]
                demo_two_corr_balance = numpy.corrcoef(cust_balancenormal, cust_demo_two)[1,0]


                #Demographic to Account Number Correlations
                demo_one_corr_acct_a = numpy.corrcoef(num_acct_A, cust_demo_one)[1,0]
                demo_one_corr_acct_b = numpy.corrcoef(num_acct_B, cust_demo_one)[1,0]
                demo_two_corr_acct_a = numpy.corrcoef(num_acct_A, cust_demo_two)[1,0]
                demo_two_corr_acct_b = numpy.corrcoef(num_acct_B, cust_demo_two)[1,0]

                #Marketing Response Channel One
                mark_one_corr_acct_a = numpy.corrcoef(num_acct_A, out_mark_channel_one)[1, 0]
                mark_one_corr_acct_b = numpy.corrcoef(num_acct_B, out_mark_channel_one)[1, 0]
                mark_one_corr_balance = numpy.corrcoef(cust_balancenormal, out_mark_channel_one)[1, 0]

                #Marketing Response Channel Two
                mark_two_corr_acct_a = numpy.corrcoef(num_acct_A, out_service_channel_two)[1, 0]
                mark_two_corr_acct_b = numpy.corrcoef(num_acct_B, out_service_channel_two)[1, 0]
                mark_two_corr_balance = numpy.corrcoef(cust_balancenormal, out_service_channel_two)[1, 0]

                #Marketing Response Channel Three
                mark_three_corr_acct_a = numpy.corrcoef(num_acct_A, out_mark_channel_three)[1, 0]
                mark_three_corr_acct_b = numpy.corrcoef(num_acct_B, out_mark_channel_three)[1, 0]
                mark_three_corr_balance = numpy.corrcoef(cust_balancenormal, out_mark_channel_three)[1, 0]

                #Marketing Response Channel Four
                mark_four_corr_acct_a = numpy.corrcoef(num_acct_A, out_mark_channel_four)[1, 0]
                mark_four_corr_acct_b = numpy.corrcoef(num_acct_B, out_mark_channel_four)[1, 0]
                mark_four_corr_balance = numpy.corrcoef(cust_balancenormal, out_mark_channel_four)[1, 0]


                #Result Correlations For Exporting to CSV of all Correlations
                result_correlation = [(demo_one_corr_balance),(demo_two_corr_balance),(demo_one_corr_acct_a),(demo_one_corr_acct_b),(demo_two_corr_acct_a),(demo_two_corr_acct_b),(mark_one_corr_acct_a),(mark_one_corr_acct_b),(mark_one_corr_balance),
                                      (mark_two_corr_acct_a),(mark_two_corr_acct_b),(mark_two_corr_balance),(mark_three_corr_acct_a),(mark_three_corr_acct_b),(mark_three_corr_balance),(mark_four_corr_acct_a),(mark_four_corr_acct_b),
                                      (mark_four_corr_balance)]
                result_correlation_nan_nuetralized = numpy.nan_to_num(result_correlation)
                c.writerow(result_correlation)

        result_correlation_combined = emptylist.append([result_correlation])
        cust_delete_list = [0,x_customer,1]
        overalldata = numpy.delete(overalldata, (cust_delete_list), axis=0)