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(, , 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