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，以便我可以进一步分析数据。

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

然而，当我尝试使用Savetxt保存时，我只得到我计算的最终值。

我想我把我的for循环放在了错误的地方，应该去哪里？我试过检查其他问题，但它并没有给它带来太多的启示。

编辑：我已经尝试将写入与外部for循环和内部for循环对齐，如同建议，两者都产生相同的结果。

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，然后在最终保存和打印中使用它。显然它会打印最后一个循环的结果。

与此同时，您将result_correlation_combined附加到x循环之外，靠近x_customer循环的趋势。但是你没有对列表做任何事情。

最后在你使用x_customer的{{1}}循环中，但我看不到任何进一步的使用。

暂时忘掉overalldata，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。我现在能够写入文件。但是，我在完成迭代次数方面遇到了麻烦，我将在不同的问题中将其发布，因为它是无关的。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)

Numpy Savetxt覆盖，无法找出放置循环的位置

2 个答案: