Apply a function on a pandas dataframe through a list of datetime windows

Question

I'd like to calculate some aggregates over a pandas DataFrame. My DataFrame has no an uniform sample time but I want to calculate aggregates over uniform sample times.

For example: I want to calculate the mean of the last 7 days, each day. So I want the resulting dataframe to have a datetime index daily.

I tried to implement it with a map-reduce structure but it was 40 times slower than a pandas implementation (using some specific cases).

My question is, do you know if there is any way to do this using pandas built-in functions?

An input DataFrame could be:

                            a         b         c
2010-01-01 00:00:00  0.957828  0.784962  0.801670
2010-01-01 00:00:06  0.669214  0.484439  0.479857
2010-01-01 00:00:18  0.537689  0.222179  0.995624
2010-01-01 00:01:15  0.339822  0.787626  0.144389
2010-01-01 00:02:21  0.705167  0.163373  0.317012
...                       ...       ...       ...
2010-03-14 23:57:35  0.799490  0.692932  0.050606
2010-03-14 23:58:40  0.380406  0.825227  0.643480
2010-03-14 23:58:43  0.838390  0.701595  0.632378
2010-03-14 23:59:01  0.604610  0.965274  0.503141
2010-03-14 23:59:02  0.320855  0.937064  0.192669

And the function should output something like this:

                   a         b         c
2010-01-01  0.957828  0.784962  0.801670
2010-01-02  0.499331  0.499944  0.505271
2010-01-03  0.499731  0.498455  0.503352
2010-01-04  0.499632  0.499328  0.502895
2010-01-05  0.500119  0.500299  0.502169
...              ...       ...       ...
2010-03-10  0.499813  0.499680  0.501154
2010-03-11  0.499965  0.500226  0.501582
2010-03-12  0.500644  0.500720  0.501243
2010-03-13  0.500439  0.500264  0.501203
2010-03-14  0.500776  0.500334  0.501048

Where each value corresponds to the mean of the last 7 days.

Answer 1

I know it's an old thread but maybe this answer would be helpful to someone.
I think it's:

df.rolling(window=your_window_value).mean()    # Simple Moving Average

That could be used.
But it will work only if for each day you have the same number of rows. In such case you could use window value equal to 7 x number of rows per day .
If for each day you have different number of rows then we would probably need different solution. But scenario was not fully defined in the question...