Notebook
In [6]:
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.factset.ownership import Form3AggregatedTrades
from quantopian.pipeline.data.factset.ownership import Form4and5AggregatedTrades
from quantopian.pipeline.filters import QTradableStocksUS 
from quantopian.pipeline.domain import US_EQUITIES
from quantopian.pipeline.factors import PercentChange
from quantopian.research import run_pipeline
import quantopian.pipeline.data.factset.estimates as fe
import pandas as pd

QTU = QTradableStocksUS()

fq1_eps_cons = fe.PeriodicConsensus.slice('EPS', 'qf', 1)
fq1_eps_cons_up = fq1_eps_cons.up.latest
fq1_eps_cons_down = fq1_eps_cons.down.latest   

# Add columns for the fractions to the result.
pipe = Pipeline(
    columns={
        'fq1_eps_cons_up': fq1_eps_cons_up,
        'fq1_eps_cons_down': fq1_eps_cons_down,
    },
    domain=US_EQUITIES,
    screen=QTU,
)

df = run_pipeline(pipe, '2014-04-01', '2017-12-19')
# df











    






Pipeline Execution Time: 29.28 Seconds













        






In [7]:



    


dfnew=pd.DataFrame()



    













In [8]:



    


dfnew["null_records"]=df.isnull().sum()
dfnew["notnull_records"]=df.notnull().sum()
dfnew["total_records"]=df.isnull().sum()+df.notnull().sum()
dfnew["null_records/total_records"]=dfnew["null_records"]/dfnew["total_records"]



    













In [9]:



    


dfnew.sort_values(by="null_records/total_records",ascending=False)



    















    
Out[9]:










  
    

      
      null_records
      notnull_records
      total_records
      null_records/total_records
    

  
  
    

      fq1_eps_cons_down
      27772
      1949897
      1977669
      0.014043
    

    

      fq1_eps_cons_up
      27772
      1949897
      1977669
      0.014043
    

  






















In [10]:



    


df.head()



    















    
Out[10]:










  
    

      
      
      fq1_eps_cons_down
      fq1_eps_cons_up
    

  
  
    

      2014-04-01 00:00:00+00:00
      Equity(2 [ARNC])
      3.0
      6.0
    

    

      Equity(24 [AAPL])
      38.0
      4.0
    

    

      Equity(31 [ABAX])
      8.0
      0.0
    

    

      Equity(39 [DDC])
      0.0
      2.0
    

    

      Equity(41 [ARCB])
      12.0
      3.0
    

  






















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