human
Solve the following python programming problem given the following code:
# Custom Factor 3 : Price to Trailing 12 Month Cash Flows
class Price_to_TTM_Cashflows(CustomFactor):
    inputs = [morningstar.valuation_ratios.pcf_ratio]
    window_length = 1
    
    def compute(self, today, assets, out, pcf):
        out[:] = pcf[-1] 
        
temp_pipe_3 = Pipeline() 
temp_pipe_3.add(Price_to_TTM_Cashflows(), 'Price / TTM Cashflows')
temp_pipe_3.add(Sector(), 'Sector')
temp_res_3 = run_pipeline(temp_pipe_3, '2015-06-06', '2015-06-06')

# clean extreme data points
cashflows_frame = temp_res_3[temp_res_3['Price / TTM Cashflows'] < 100]

# create boxplot by sector
cashflows_frame.boxplot(column='Price / TTM Cashflows', by='Sector');
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

From this graph it is easy to see that the makeup of Price to Earnings Ratios for different sectors differs substantially. Therefore, an industry-relative metric will be a more discerning value than a universe-wide one.
Industry-Relative Price to TTM Cash Flows
This is calculated as a simple ratio between price per share and TTM free cash flows per share standardized over industry groups.
Unlike earnings, a company's cash flows are a very definite metric and is not subject to accounting irregularities. However, this, too, can benefit from an industry-relative approach as different industries have different regular cash flows.
We will apply the same methodology as the previous factor in order to examine the data.