我正在尝试将变量聚合到时隙上，但我不确定如何以Pythonic方式执行此操作。我的计划是每十秒聚合一次。 我的时间数据是第一列，从午夜开始以秒为单位。第二列是我想要聚合的变量。大多数条目为零，但其他条目是我想要求和的实际数字。
因此，我们的想法是从第1列开始每隔10秒对第2列求和。 矩阵向量将具有第一列的每十秒时间和第二列的聚合变量的时间。
我的第一个想法是在第二列做一个while循环，并在我们在同一时段内时对所有数字求和。并将其放入for循环中，每增加10秒。这似乎非常沉重和缓慢。
非常感谢任何建议。谢谢。
示例数据：
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答案 0 :(得分：3)
考虑以下方法：
urlpatterns = [
url(r'^$', FlowListView.as_view(), name='flow-list'),
]
来源DF：
import pandas as pd
df = pd.read_csv(filename, header=None, names=['ts','val'])
In [31]: df.groupby(df['ts']//10*10)['val'].sum()
Out[31]:
ts
34200.0 1806.0
34210.0 907.0
34220.0 100.0
34230.0 -201.0
Name: val, dtype: float64
In [33]: df
Out[33]:
ts val
0 34200.095354 0.0
1 34200.095354 0.0
2 34200.095354 0.0
3 34200.095354 0.0
4 34200.095354 0.0
5 34200.095354 0.0
6 34200.095354 0.0
7 34200.095354 0.0
8 34200.095354 0.0
9 34200.095354 0.0
.. ... ...
237 34230.365951 0.0
238 34233.152929 -1.0
239 34233.152986 -100.0
240 34233.152996 -100.0
241 34233.153011 0.0
242 34233.154379 0.0
243 34233.154699 -200.0
244 34233.155680 0.0
245 34233.176736 100.0
246 34233.187006 100.0
[247 rows x 2 columns]
- 将时间戳最多为10秒，因此我们可以在以后使用它进行灌浆：
df['ts']//10*10