How to parallelize this for loop (or make it faster) using pandas or dask

问题

I want to make this loop significantly faster. It is calculating the move in a row for each feater. The function here is only applied to one column. Later, I am looping through each feature (df.columns) and applying this function.

def move_iar(df, feature):

    lst=[]
    prev_move_iar = 0

    for move in df[feature]:
        if np.isnan(move):
            move_iar = 0
            lst.append(move_iar)
            prev_move_iar = move_iar
        else:
            if move == 0:
                move_iar = prev_move_iar
                lst.append(move_iar)
                prev_move_iar = move_iar
            elif (move >= 0 and prev_move_iar >= 0) or (move <= 0 and prev_move_iar <= 0):
                move_iar = move + prev_move_iar
                lst.append(move_iar)
                prev_move_iar = move_iar
            elif (move < 0 and prev_move_iar >= 0) or (move > 0 and prev_move_iar <= 0):
                move_iar = move
                lst.append(move_iar)
                prev_move_iar = move_iar

    return pd.DataFrame(lst, index=df.index, columns=[feature]).rename(columns={feature : feature + 'IAR'})

来源：https://stackoverflow.com/questions/58192507/how-to-parallelize-this-for-loop-or-make-it-faster-using-pandas-or-dask