There is df2 Pandas dataframe: surgery age rectal_temp pulse respiratory_rate extremities_temp pain outcome 4 2.0 1 NaN NaN NaN 2.0 2.0 1.0 6 1.0 1 NaN 60.0 NaN 3.0 NaN 2.0 7 2.0 1 NaN 80.0 36.0 3.0 4.0 3.0 15 1.0 9 NaN 128.0 36.0 3.0 4.0 2.0 27 1.0 1 NaN NaN NaN NaN NaN 2.0 33 1.0 1 NaN 100.0 30.0 3.0 5.0 1.0 34 2.0 1 NaN 104.0 24.0 4.0 4.0 3.0 39 1.0 1 NaN 88.0 NaN 3.0 5.0 2.0 Write a function that: 1) takes as a parameter the column name (default = 'rectal_temp') 2) calculate the correlation of this column with other columns: pulse, respiratory_rate, extremities_temp, pain_outcome 3) fill all NaNs in the 'rectal_temp' column with the average values multiplied by the correlation weight

def fill_na(df, column_name):
    corr = df.corr()[column_name]
    new_value = corr.mean() * df[column_name].mean()
    df[column_name] = df[column_name].fillna(new_value)
    return df
    
fill_na(df2, column_name='rectal_temp')