How can I efficiently create a new column in a pandas DataFrame based on another column's rolling mean over a period of 30 days?

I'm working with a pandas dataframe and working with stock data. I'm trying to create another column based off of another column while trying to avoid the slow for loop. I used a while loop to create a new column of the mean price of the past 30 days because I can't figure out another way...

    VOLUME_SERIES = DF.loc[:, 'a_volume']
    VOLUME_SERIES_IND = DF.loc[:, 'a_volume'].index.to_numpy()
    MAX_INDEX = np.max(VOLUME_SERIES_IND)

    period = 30
    period = period - 1
    START = 0
    STOP = START + period

    DF['mean_a_volume'] = np.nan

    while STOP <= MAX_INDEX:
        mean_vol = np.mean(DF.loc[START:STOP, 'a_volume'])
        DF.loc[START, 'mean_a_volume'] = mean_vol
        START = START + 1
        STOP = START + period

I actually wish that I could use the .apply() method and np.where() while somehow passing in the index of the row, telling it to not apply anything if the index is greater than MAX_INDEX - period but can't seem to figure out how to get the index of the row in a series...I tried getting the indexes of the series with .apply(lambda x: print(index[x]), it looked ok for the most part but for some indexes it had two indexes so I knew something wasn't quite right and to avoid it...Really stuck here on this and don't know how else to proceed...Appreciate any help. Thanks.

You can use rolling.

import pandas as pd
column = [1, 2, 3, 4, 5, 6, 7, 8, 9]
df = pd.DataFrame()
df['column'] = column
df['mean'] = df['column'].rolling(2).mean()

You can use df.rolling method to compute the rolling mean. It is vectorized so it will be a bit more efficient than looping. Here is the documentation for it.

Here is the updated code:

mean_volume = DF.rolling(window=30).mean()