Numpy Savetxt 覆盖，无法确定放置循环的位置
Numpy Savetxt Overwriting, Cannot Figure Out Where to Place Loop
我正在创建一个程序来计算客户数据之间的相关性。我想将相关值打印到 CSV 文件中，以便进一步分析数据。
我已经成功地让我的程序循环遍历所有客户（每个客户 12 个月的数据），同时计算他们与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。
但是，当我尝试使用 Savetxt 保存时，我只得到了我计算的最终值。
我想我把 for 循环放在了错误的地方，它应该放在哪里？我试过检查其他问题，但并没有说明太多问题。
编辑：我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
这部分代码很草率：
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
你在最里面的循环中设置了result_correlation，然后你在最后的保存打印中使用它。显然它会打印最后一个循环的结果。
同时将其附加到 result_correlation_combined，在 x 循环之外，靠近 x_customer 循环。但是您不对该列表执行任何操作。
最后在 x_customer 循环中，您使用 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。
我采纳了上述 poster 的建议并更正了我的代码。我现在可以写入文件了。但是，我在完成迭代次数方面遇到了问题，我将 post 在另一个问题中解决，因为它是不相关的。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)
我正在创建一个程序来计算客户数据之间的相关性。我想将相关值打印到 CSV 文件中，以便进一步分析数据。
我已经成功地让我的程序循环遍历所有客户（每个客户 12 个月的数据），同时计算他们与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。
但是，当我尝试使用 Savetxt 保存时，我只得到了我计算的最终值。
我想我把 for 循环放在了错误的地方，它应该放在哪里？我试过检查其他问题，但并没有说明太多问题。
编辑：我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
这部分代码很草率：
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
你在最里面的循环中设置了result_correlation，然后你在最后的保存打印中使用它。显然它会打印最后一个循环的结果。
同时将其附加到 result_correlation_combined，在 x 循环之外，靠近 x_customer 循环。但是您不对该列表执行任何操作。
最后在 x_customer 循环中，您使用 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。
我采纳了上述 poster 的建议并更正了我的代码。我现在可以写入文件了。但是，我在完成迭代次数方面遇到了问题，我将 post 在另一个问题中解决，因为它是不相关的。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)