Get moving average of list of panda dataframes starting from first row, not last-CodePudding

I have a list of panda dataframe;

I would like to convert it into a panda dataframe that looks like this;

I tried something like panda_series.rolling(window=3).mean(). However, the problem is that the moving average is calculated starting from the last row. What I want is to have the moving average to be calculated starting from the first row.

CodePudding user response：

You can compute the rolling on the reversed Series, and set the min_periods to 1:

df['MA3'] = df['value'][::-1].rolling(window=3, min_periods=1).mean()

Output:

   value   MA3
0     12  13.0
1     13  14.0
2     14  14.5
3     15  15.0

Note that it is not necessary to reverse the output if you assign it back as a column due to the fact that pandas performs index alignement before inserting the data.

If you're working with an isolated Series, however, you will need to use:

s = df['value'][::-1].rolling(window=3, min_periods=1).mean()[::-1]

# or
# s2 = s1[::-1].rolling(window=3, min_periods=1).mean()[::-1]

CodePudding user response：

I would like to build on the answer by @mozway. Credit goes to him.

To get the right order in the panda series as required in the question, here's the code.

df['MA3'] = df['value'][::-1].rolling(window=3, min_periods=1).mean()
df['MA3'] = df['MA3'][::-1]