import pyspark.pandas as ps

#  Add EWMA features to the DataFrame for the specified alpha values
def add_ewma_features(df, alphas):
    for alpha in alphas:
        ewma_col_name = f"ewma_power_w{str(alpha).replace('.', '')}"
        windowSpec = Window.orderBy("Date")
        df[ewma_col_name] = df.Total_global_active_power.ewm(alpha=alpha).mean().round(2)
    return df

alphas = [0.2, 0.8]
# Convert into a pandas-on-Spark DataFrame, to use EWM function
df2_pd = df2.pandas_api()
df2_pd = add_ewma_features(df2_pd, alphas)
# Convert back to a Spark DataFrame
df2 = df2_pd.to_spark()

df2.select("Date", "Total_global_active_power", "ewma_power_w02", "ewma_power_w08").sort("Date", ascending=False).show(5)