Fastest way to get rolling averages in pandas?

Question

I have a list of nodes (about 2300 of them) that have hourly price data for about a year. I have a script that, for each node, loops through the times of the day to create a 4-hour trailing average, then groups the averages by month and hour. Finally, these hours in a month are averaged to give, for each month, a typical day of prices. I'm wondering if there is a faster way to do this because what I have seems to take a significant amount of time (about an hour). I also save the dataframes as csv files for later visualization (that's not the slow part).

df (before anything is done to it)
        Price_Node_Name      Local_Datetime_HourEnding   Price      Irrelevant_column

0       My-node                 2016-08-17 01:00:00        20.95          EST
1       My-node                 2016-08-17 02:00:00        21.45          EST
2       My-node                 2016-08-17 03:00:00        25.60          EST

df_node (after the groupby as it looks going to csv)
Month        Hour             MA
1            0                23.55
1            1                23.45
1            2                21.63


 for node in node_names:
     df_node = df[df['Price_Node_Name'] == node]
     df_node['MA'] = df_node['Price'].rolling(4).mean()
     df_node = df_node.groupby([df_node['Local_Datetime_HourEnding'].dt.month, 
                      df_node['Local_Datetime_HourEnding'].dt.hour]).mean()
     df_node.to_csv('%s_rollingavg.csv' % node)

I get an weak error warning me about SetWithCopy, but I haven't quite figured out how to use .loc here since the column ['MA'] doesn't exist until I create it in this snippet and any way I can think of to create it before hand and fill it seems slower than what I have. Could be totally wrong though. Any help would be great.

python 3.6

Answer 1

edit: I might have misread the question here, hopefully this at least sparks some ideas for the solution.

I think it is useful to have the index as the datetime column when working with time series data in Pandas.

Here is some sample data:

Out[3]:
                          price
date
2015-01-14 00:00:00  155.427361
2015-01-14 01:00:00  205.285202
2015-01-14 02:00:00  205.305021
2015-01-14 03:00:00  195.000000
2015-01-14 04:00:00  213.102000
2015-01-14 05:00:00  214.500000
2015-01-14 06:00:00  222.544375
2015-01-14 07:00:00  227.090251
2015-01-14 08:00:00  227.700000
2015-01-14 09:00:00  243.456190

We use Series.rolling to create an MA column, ie we apply the method to the price column, with a two-period window, and call mean on the resulting rolling object:

In [4]: df['MA'] = df.price.rolling(window=2).mean()

In [5]: df
Out[5]:
                          price          MA
date
2015-01-14 00:00:00  155.427361         NaN
2015-01-14 01:00:00  205.285202  180.356281
2015-01-14 02:00:00  205.305021  205.295111
2015-01-14 03:00:00  195.000000  200.152510
2015-01-14 04:00:00  213.102000  204.051000
2015-01-14 05:00:00  214.500000  213.801000
2015-01-14 06:00:00  222.544375  218.522187
2015-01-14 07:00:00  227.090251  224.817313
2015-01-14 08:00:00  227.700000  227.395125
2015-01-14 09:00:00  243.456190  235.578095

And if you want month and hour columns, can extract those from the index:

In [7]: df['month'] = df.index.month  

In [8]: df['hour'] = df.index.hour

In [9]: df
Out[9]:
                          price          MA  month  hour
date
2015-01-14 00:00:00  155.427361         NaN      1     0
2015-01-14 01:00:00  205.285202  180.356281      1     1
2015-01-14 02:00:00  205.305021  205.295111      1     2
2015-01-14 03:00:00  195.000000  200.152510      1     3
2015-01-14 04:00:00  213.102000  204.051000      1     4
2015-01-14 05:00:00  214.500000  213.801000      1     5
2015-01-14 06:00:00  222.544375  218.522187      1     6
2015-01-14 07:00:00  227.090251  224.817313      1     7
2015-01-14 08:00:00  227.700000  227.395125      1     8
2015-01-14 09:00:00  243.456190  235.578095      1     9

Then we can use groupby :

In [11]: df.groupby([
    ...:     df['month'],
    ...:     df['hour']
    ...: ]).mean()[['MA']]
Out[11]:
                    MA
month hour
1     0            NaN
      1     180.356281
      2     205.295111
      3     200.152510
      4     204.051000
      5     213.801000
      6     218.522187
      7     224.817313
      8     227.395125
      9     235.578095

Answer 2

Here's a few things to try:

set 'Price_Node_name' to the index before the loop

df.set_index('Price_Node_name', inplace=True)
for node in node_names:
    df_node = df[node]

use sort=False as a kwarg in the groupby

df_node.groupby(..., sort=False).mean()

Perform the rolling average AFTER the groupby, or don't do it at all--I don't think you need it in your case. Averaging the hourly totals for a month will give you the expected values for a typical day, which is what you desire. If you still want the rolling average, perform it on the averaged hourly totals for each month.