It's probably easiest to use the pandas sum method to get a rank or score for assets where the score is the sum of a number of conditions. It's a little involved (a custom factor) to sum conditions inside of pipeline. However, outside of pipeline it can be done with a single command.

First, put all your conditions (which will be True or False) into the pipeline output. Next, simply run the pipeline and sum those conditions in the resulting dataframe. Something like this

# Define all the conditions for scoring  
return_gt_1 = Returns(window_length=5) > .1  
rerturns_top_50 = Returns(window_length=5).top(50, mask=base_universe)  
price_gt_5 = EquityPricing.close.latest > 5  
dollar_volume_gt_mean = AverageDollarVolume(window_length=10).demean(mask=(base_universe)) > 0  

    # Place the conditions into the pipeline output  
    return Pipeline(  
        columns={  
            'Weeklyreturn_gt_1': return_gt_1,  
            'rerturns_top_50': rerturns_top_50,  
            'price_gt_5': price_gt_5,  
            'dollar_volume_gt_mean': dollar_volume_gt_mean,  
        },  
        screen=base_universe  
    )  

After running the pipeline, add a column for score like this

# Add a column for 'score' to the dataframe  
# It will be the sum of the true conditions. Use 'sum' since True=1  
pipe_output['score'] = pipe_output.sum(axis=1)

One can then see all the conditions and the final score in a neat dataframe. One could plot the scores or possibly a histogram to check distribution.

Check out the attached notebook.

Hope this helps. Good luck.

Thank you, Dan. Works great.
I made a little change so that it calculates only the columns I wanted. In case anyone is interested:

column_list = ["condition1", "condition2", "condition3", "condition4"]  
result['score'] = result[column_list].sum(axis=1)