Creating a stock price prediction model

We will begin our project by processing the data present in the dataset:

1. Create a dataframe with yearly time series for each stock. Represent each year's stock price by an individual column in that dataframe. Restrict number of rows in the dataframe to 252 which is roughly the number of trading days in a year. Also add the fiscal quarter associated with each row of data as a separate column.

def get_prices_by_year(self):
   df = self.modify_first_year_data()
   for i in range(1, len(self.num_years)):
       df = pd.concat([df, pd.DataFrame(self.get_year_data(year=self.num_years[i], normalized=True))], axis=1)
   df = df[:self.num_days]
   quarter_col = []
   num_days_in_quarter = self.num_days // 4
   for j in range(0, len(self.quarter_names)):
       quarter_col.extend([self.quarter_names[j]]*num_days_in_quarter)
   quarter_col = pd.DataFrame(quarter_col)
   df = pd.concat([df, quarter_col], axis=1)
   df.columns = self.num_years + ['Quarter']
   df...