def drop_multiple_col(col_names_list, df): 
    '''
    AIM    -> Drop multiple columns based on their column names 

    INPUT  -> List of column names, df

    OUTPUT -> updated df with dropped columns 
    ------
    '''
    df.drop(col_names_list, axis=1, inplace=True)
    return df

def change_dtypes(col_int, col_float, df): 
    '''
    AIM    -> Changing dtypes to save memory

    INPUT  -> List of column names (int, float), df

    OUTPUT -> updated df with smaller memory  
    ------
    '''
    df[col_int] = df[col_int].astype('int32')
    df[col_float] = df[col_float].astype('float32')

def convert_cat2num(df):
    # Convert categorical variable to numerical variable
    num_encode = {'col_1' : {'YES':1, 'NO':0},
                  'col_2'  : {'WON':1, 'LOSE':0, 'DRAW':0}}  
    df.replace(num_encode, inplace=True)  

def check_missing_data(df):
    # check for any missing data in the df (display in descending order)
    return df.isnull().sum().sort_values(ascending=False)

def remove_col_str(df):
    # remove a portion of string in a dataframe column - col_1
    df['col_1'].replace('\n', '', regex=True, inplace=True)

    # remove all the characters after &# (including &#) for column - col_1
    df['col_1'].replace(' &#.*', '', regex=True, inplace=True)

def remove_col_white_space(df):
    # remove white space at the beginning of string 
    df[col] = df[col].str.lstrip()

def concat_col_str_condition(df):
    # concat 2 columns with strings if the last 3 letters of the first column are 'pil'
    mask = df['col_1'].str.endswith('pil', na=False)
    col_new = df[mask]['col_1'] + df[mask]['col_2']
    col_new.replace('pil', ' ', regex=True, inplace=True)  # replace the 'pil' with emtpy space

def convert_str_datetime(df): 
    '''
    AIM    -> Convert datetime(String) to datetime(format we want)

    INPUT  -> df

    OUTPUT -> updated df with new datetime format 
    ------
    '''
    df.insert(loc=2, column='timestamp', value=pd.to_datetime(df.transdate, format='%Y-%m-%d %H:%M:%S.%f'))