I am trying find something resembles a moving average but excludes the previous set of values Below snapshot of excel sheet shows the average with a window of 2.

snapshot

I tried using rolling and expand command in pandas but they include the previous value. Are there any other methods that can be used from pandas

CodePudding user response：

An easy way of doing this is to convert the column to a list and then calculate the moving averages using a loop (or more precisely a "list comprehension").

# Creating a DataFrame (like the one you have)
values_col = [i * 10 for i in range(10)]
df = pd.DataFrame({'values': values_col})

window_size = 2
hop_size = 2
n = len(df.index)

values_moving_average = [sum(df['values'].tolist()[i - window_size : i]) / window_size for i in range(window_size, n   1, hop_size)]

Then you will have the moving averages you wanted in values_moving_average list:

# result
[5.0, 25.0, 45.0, 65.0, 85.0]

Here, window_size is the size of the window, and hop_size is a variable you can use specify the distance between the first indices of two consecutive windows. For example if hop_size = 1, then we will get the average of the following windows in a list:

(0, 10), (10, 20), (20, 30), ..., (80, 90)

But, with hop_size = 2, we will have the following windows:

(0, 10), (20, 30), (40, 50), ..., (80, 90)