original_docstring: 
      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. We assume those
          companies have no fixed costs when calculating the marginal
          costs.
  
      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: 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: 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(file):
      """
      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. We assume those
          companies have no fixed costs when calculating the marginal
          costs.
  
      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'])
  
          for total_cost in total_costs:
              marginal_costs = total_cost.diff() / total_prod.diff()
              marginal_costs.name = f"M{total_cost.name}"
  
              ols_mc = regress_info(total_prod[1:], marginal_costs[1:])
              companies.append(create_est_company(ols_mc))
  
          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))
          return ols_demand, demand_estimate, companies
      except Exception:
          print("Failed while importing data")
          exit(1)
code_tokens: def estimate_curves ( 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' ] ) for total_cost in total_costs : marginal_costs = total_cost . diff ( ) / total_prod . diff ( ) marginal_costs . name = f"M{total_cost.name}" ols_mc = regress_info ( total_prod [ 1 : ] , marginal_costs [ 1 : ] ) companies . append ( create_est_company ( ols_mc ) ) 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 ) ) return ols_demand , demand_estimate , companies except Exception : print ( "Failed while importing data" ) exit ( 1 )
short_docstring: Given a file with data in a specific format - required by my project,
  returns a Demand and Company_List.
short_docstring_tokens: Given a file with data in a specific format - required by my project returns a Demand and Company_List .
comment: """
      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. We assume those
          companies have no fixed costs when calculating the marginal
          costs.
  
      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