original_docstring: 
      WORK IN PROGRESS -- NOT OPTIMIZED
      Given a file with data in a specific format - required by my project,
      returns a Demand and Company_List.
  
      Args:
          file : Requires a file with columns 'P', 'Q', 'C1',
          'C2' ,'C3'. The combinations of 'P' and 'Q' are market
          equilibrium points. 'C1','C2','C3' are the total costs of
          the companies at 'Q' level of production.
  
      Returns:
          Demand: The parameters of the inverse demand in the current market
          Company_List: The parameters of the marginal cost for the given Companies.
      
docstring: WORK IN PROGRESS -- NOT OPTIMIZED
  Given a file with data in a specific format - required by my project,
  returns a Demand and Company_List.
  
  
  
  The parameters of the inverse demand in the current market
  Company_List: The parameters of the marginal cost for the given Companies.
docstring_tokens: WORK IN PROGRESS -- NOT OPTIMIZED Given a file with data in a specific format - required by my project returns a Demand and Company_List . The parameters of the inverse demand in the current market Company_List : The parameters of the marginal cost for the given Companies .
code: def estimate_curves_alt(file):
      """
      WORK IN PROGRESS -- NOT OPTIMIZED
      Given a file with data in a specific format - required by my project,
      returns a Demand and Company_List.
  
      Args:
          file : Requires a file with columns 'P', 'Q', 'C1',
          'C2' ,'C3'. The combinations of 'P' and 'Q' are market
          equilibrium points. 'C1','C2','C3' are the total costs of
          the companies at 'Q' level of production.
  
      Returns:
          Demand: The parameters of the inverse demand in the current market
          Company_List: The parameters of the marginal cost for the given Companies.
      """
  
      companies: CompanyList = []
  
      # noinspection PyBroadException
      try:
          df = pd.read_csv(file)
          total_prod: pd.Series = df['Q']
          prices: pd.Series = df['P']
          total_costs = (df['C1'], df['C2'], df['C3'])
  
          ols_demand = regress_info(prices, total_prod)
          a, b = ols_demand.params[0], ols_demand.params[1]
          demand_estimate: Demand = (abs(a / b), 1 / abs(b))
  
          for total_cost in total_costs:
              marginal_costs = total_cost.diff() / total_prod.diff()
              marginal_costs.name = f"M{total_cost.name}"
              df[f"M{total_cost.name}"] = marginal_costs
              # all_marginal_costs.append(marginal_costs)
  
          df['q1'], df['q2'], df['q3'] = estimate_comp_productions(
              demand_estimate, (df['MC1'], df['MC2'], df['MC3']))
  
          for company_prod, marginal_costs in ((df['q1'], df['MC1']),
                                               (df['q2'], df['MC2']),
                                               (df['q3'], df['MC3'])):
              ols_mc = regress_info(company_prod[1:], marginal_costs[1:])
              companies.append(create_est_company(ols_mc))
  
          return ols_demand, demand_estimate, companies
  
      except Exception:
          print("Failed while importing data")
          exit()
code_tokens: def estimate_curves_alt ( file ) : companies : CompanyList = [ ] try : df = pd . read_csv ( file ) total_prod : pd . Series = df [ 'Q' ] prices : pd . Series = df [ 'P' ] total_costs = ( df [ 'C1' ] , df [ 'C2' ] , df [ 'C3' ] ) ols_demand = regress_info ( prices , total_prod ) a , b = ols_demand . params [ 0 ] , ols_demand . params [ 1 ] demand_estimate : Demand = ( abs ( a / b ) , 1 / abs ( b ) ) for total_cost in total_costs : marginal_costs = total_cost . diff ( ) / total_prod . diff ( ) marginal_costs . name = f"M{total_cost.name}" df [ f"M{total_cost.name}" ] = marginal_costs df [ 'q1' ] , df [ 'q2' ] , df [ 'q3' ] = estimate_comp_productions ( demand_estimate , ( df [ 'MC1' ] , df [ 'MC2' ] , df [ 'MC3' ] ) ) for company_prod , marginal_costs in ( ( df [ 'q1' ] , df [ 'MC1' ] ) , ( df [ 'q2' ] , df [ 'MC2' ] ) , ( df [ 'q3' ] , df [ 'MC3' ] ) ) : ols_mc = regress_info ( company_prod [ 1 : ] , marginal_costs [ 1 : ] ) companies . append ( create_est_company ( ols_mc ) ) return ols_demand , demand_estimate , companies except Exception : print ( "Failed while importing data" ) exit ( )
short_docstring: WORK IN PROGRESS -- NOT OPTIMIZED
  Given a file with data in a specific format - required by my project,
  returns a Demand and Company_List.
short_docstring_tokens: WORK IN PROGRESS -- NOT OPTIMIZED Given a file with data in a specific format - required by my project returns a Demand and Company_List .
comment: """
      WORK IN PROGRESS -- NOT OPTIMIZED
      Given a file with data in a specific format - required by my project,
      returns a Demand and Company_List.
  
      Args:
          file : Requires a file with columns 'P', 'Q', 'C1',
          'C2' ,'C3'. The combinations of 'P' and 'Q' are market
          equilibrium points. 'C1','C2','C3' are the total costs of
          the companies at 'Q' level of production.
  
      Returns:
          Demand: The parameters of the inverse demand in the current market
          Company_List: The parameters of the marginal cost for the given Companies.
      """$SEP$# noinspection PyBroadException$SEP$# all_marginal_costs.append(marginal_costs)