2017-10-12 141 views
0

我有数据帧,我已经将它改成字典列表:URLEncode的名单

df = data.to_dict(orient = "records") 

输出:

[{'MAIN KITCHEN': 9.6, 'Time': ' 05/01/2017 00:05:00'}, 
{'MAIN KITCHEN': 9.6, 'Time': ' 05/01/2017 00:10:00'}, 
{'MAIN KITCHEN': 9.6, 'Time': ' 05/01/2017 00:15:00'}, 
{'MAIN KITCHEN': 11.2, 'Time': ' 05/01/2017 00:20:00'}, 
{'MAIN KITCHEN': 11.2, 'Time': ' 05/01/2017 00:25:00'}, 
{'MAIN KITCHEN': 12.8, 'Time': ' 05/01/2017 00:30:00'}, 
{'MAIN KITCHEN': 9.6, 'Time': ' 05/01/2017 00:35:00'}, 
{'MAIN KITCHEN': 11.2, 'Time': ' 05/01/2017 00:40:00'}, 
{'MAIN KITCHEN': 12.8, 'Time': ' 05/01/2017 00:45:00'}] 

P.S:我想只有这样我的数据。

我想将此输出编码为url或查询字符串。

我想这:

param = urllib.urlencode(df) 

但我得到一个错误:

TypeError: not a valid non-string sequence or mapping object 

谁能告诉我正确的方式做到这一点?

+0

什么是预期的输出? – MedAli

+0

@MedAli,%5B%7B%27MAIN + KITCHEN%27%3A + 9.6%2C +%27%27%3A +%27 + 05%2F01%2F2017 + 00%3A05%3A00%27%7D%2C +%0D%0A + %7B%27MAIN +厨房%27%3A + 9.6%2C +%27时间%27%3A +%27 + 05%2F01%2F2017 – Dheeraj

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检查我的答案,并让我知道你是否有任何问题? – MedAli

回答

0

您需要遍历字典列表,并在其中的每一个上应用urllib.urlencode

In [46]: [urllib.urlencode(d) for d in df.to_dict(orient='records')] 
Out[46]: 
['Time=+05%2F01%2F2017+00%3A05%3A00&MAIN+KITCHEN=9.6', 
'Time=+05%2F01%2F2017+00%3A10%3A00&MAIN+KITCHEN=9.6', 
'Time=+05%2F01%2F2017+00%3A15%3A00&MAIN+KITCHEN=9.6', 
'Time=+05%2F01%2F2017+00%3A20%3A00&MAIN+KITCHEN=11.2', 
'Time=+05%2F01%2F2017+00%3A25%3A00&MAIN+KITCHEN=11.2', 
'Time=+05%2F01%2F2017+00%3A30%3A00&MAIN+KITCHEN=12.8', 
'Time=+05%2F01%2F2017+00%3A35%3A00&MAIN+KITCHEN=9.6', 
'Time=+05%2F01%2F2017+00%3A40%3A00&MAIN+KITCHEN=11.2', 
'Time=+05%2F01%2F2017+00%3A45%3A00&MAIN+KITCHEN=12.8'] 

您还可以在转换之前,这样做是为了词典:

In [54]: df.apply(lambda x: urllib.urlencode(dict(x)), axis=1) 
Out[54]: 
0 Time=+05%2F01%2F2017+00%3A05%3A00&MAIN+KITCHEN... 
1 Time=+05%2F01%2F2017+00%3A10%3A00&MAIN+KITCHEN... 
2 Time=+05%2F01%2F2017+00%3A15%3A00&MAIN+KITCHEN... 
3 Time=+05%2F01%2F2017+00%3A20%3A00&MAIN+KITCHEN... 
4 Time=+05%2F01%2F2017+00%3A25%3A00&MAIN+KITCHEN... 
5 Time=+05%2F01%2F2017+00%3A30%3A00&MAIN+KITCHEN... 
6 Time=+05%2F01%2F2017+00%3A35%3A00&MAIN+KITCHEN... 
7 Time=+05%2F01%2F2017+00%3A40%3A00&MAIN+KITCHEN... 
8 Time=+05%2F01%2F2017+00%3A45%3A00&MAIN+KITCHEN... 
dtype: object 

In [55]: df.apply(lambda x: urllib.urlencode(dict(x)), axis=1).tolist() 
Out[55]: 
['Time=+05%2F01%2F2017+00%3A05%3A00&MAIN+KITCHEN=9.6', 
'Time=+05%2F01%2F2017+00%3A10%3A00&MAIN+KITCHEN=9.6', 
'Time=+05%2F01%2F2017+00%3A15%3A00&MAIN+KITCHEN=9.6', 
'Time=+05%2F01%2F2017+00%3A20%3A00&MAIN+KITCHEN=11.2', 
'Time=+05%2F01%2F2017+00%3A25%3A00&MAIN+KITCHEN=11.2', 
'Time=+05%2F01%2F2017+00%3A30%3A00&MAIN+KITCHEN=12.8', 
'Time=+05%2F01%2F2017+00%3A35%3A00&MAIN+KITCHEN=9.6', 
'Time=+05%2F01%2F2017+00%3A40%3A00&MAIN+KITCHEN=11.2', 
'Time=+05%2F01%2F2017+00%3A45%3A00&MAIN+KITCHEN=12.8']