Finding recessions in US GDP data using PANDAS












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$begingroup$


For an assignment, I am identifying the first quarter of the 2008 recession in the United States. The Excel data I'm using can be downloaded here: gdplev.xls. How can I improve this pandas code to make it more idiomatic or optimized? 



def get_recession_start():

    '''Returns the year and quarter of the recession start time as a 

    string value in a format such as 2005q3'''

    GDP_df = pd.read_excel("gdplev.xls", 

                           names=["Quarter", "GDP in 2009 dollars"], 

                           parse_cols = "E,G", 

                           skiprows = 7)

    GDP_df = GDP_df.query("Quarter >= '2000q1'")

    GDP_df["Growth"] = GDP_df["GDP in 2009 dollars"].pct_change()

    GDP_df = GDP_df.reset_index(drop=True)

    # recession defined as two consecutive quarters of negative growth

    GDP_df["Recession"] = (GDP_df.Growth < 0) & (GDP_df.Growth.shift(-1) < 0) 

    return GDP_df.iloc[GDP_df["Recession"].idxmax()]["Quarter"]

get_recession_start()





python python-3.x homework pandas







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    1












    $begingroup$


    For an assignment, I am identifying the first quarter of the 2008 recession in the United States. The Excel data I'm using can be downloaded here: gdplev.xls. How can I improve this pandas code to make it more idiomatic or optimized? 



    def get_recession_start():
    
    '''Returns the year and quarter of the recession start time as a 
    
    string value in a format such as 2005q3'''
    
    GDP_df = pd.read_excel("gdplev.xls", 
    
                           names=["Quarter", "GDP in 2009 dollars"], 
    
                           parse_cols = "E,G", 
    
                           skiprows = 7)
    
    GDP_df = GDP_df.query("Quarter >= '2000q1'")
    
    GDP_df["Growth"] = GDP_df["GDP in 2009 dollars"].pct_change()
    
    GDP_df = GDP_df.reset_index(drop=True)
    
    # recession defined as two consecutive quarters of negative growth
    
    GDP_df["Recession"] = (GDP_df.Growth < 0) & (GDP_df.Growth.shift(-1) < 0) 
    
    return GDP_df.iloc[GDP_df["Recession"].idxmax()]["Quarter"]
    
get_recession_start()





    python python-3.x homework pandas







    share|improve this question









    New contributor




    Jeremy Hadfield is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      1





      $begingroup$


      For an assignment, I am identifying the first quarter of the 2008 recession in the United States. The Excel data I'm using can be downloaded here: gdplev.xls. How can I improve this pandas code to make it more idiomatic or optimized? 



      def get_recession_start():
      
    '''Returns the year and quarter of the recession start time as a 
      
    string value in a format such as 2005q3'''
      
    GDP_df = pd.read_excel("gdplev.xls", 
      
                           names=["Quarter", "GDP in 2009 dollars"], 
      
                           parse_cols = "E,G", 
      
                           skiprows = 7)
      
    GDP_df = GDP_df.query("Quarter >= '2000q1'")
      
    GDP_df["Growth"] = GDP_df["GDP in 2009 dollars"].pct_change()
      
    GDP_df = GDP_df.reset_index(drop=True)
      
    # recession defined as two consecutive quarters of negative growth
      
    GDP_df["Recession"] = (GDP_df.Growth < 0) & (GDP_df.Growth.shift(-1) < 0) 
      
    return GDP_df.iloc[GDP_df["Recession"].idxmax()]["Quarter"]
      
get_recession_start()





      python python-3.x homework pandas







      share|improve this question









      New contributor




      Jeremy Hadfield is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      For an assignment, I am identifying the first quarter of the 2008 recession in the United States. The Excel data I'm using can be downloaded here: gdplev.xls. How can I improve this pandas code to make it more idiomatic or optimized? 



      def get_recession_start():
      
    '''Returns the year and quarter of the recession start time as a 
      
    string value in a format such as 2005q3'''
      
    GDP_df = pd.read_excel("gdplev.xls", 
      
                           names=["Quarter", "GDP in 2009 dollars"], 
      
                           parse_cols = "E,G", 
      
                           skiprows = 7)
      
    GDP_df = GDP_df.query("Quarter >= '2000q1'")
      
    GDP_df["Growth"] = GDP_df["GDP in 2009 dollars"].pct_change()
      
    GDP_df = GDP_df.reset_index(drop=True)
      
    # recession defined as two consecutive quarters of negative growth
      
    GDP_df["Recession"] = (GDP_df.Growth < 0) & (GDP_df.Growth.shift(-1) < 0) 
      
    return GDP_df.iloc[GDP_df["Recession"].idxmax()]["Quarter"]
      
get_recession_start()





      python python-3.x homework pandas




      python python-3.x homework pandas






      share|improve this question









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      Jeremy Hadfield is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









      New contributor




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      share|improve this question




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      edited 13 mins ago









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      asked 47 mins ago









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          • Running this through flake8 and pycodestyle would give you some tips, such as variable naming conventions and spacing.

          • Names like pd and GDP_df are really unhelpful. Each line should be comprehensible on its own, without having to dig backwards to figure out what a variable really is.

          • You'll want and instead of & here. & is bitwise AND.






          share|improve this answer









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            $begingroup$


            • Running this through flake8 and pycodestyle would give you some tips, such as variable naming conventions and spacing.

            • Names like pd and GDP_df are really unhelpful. Each line should be comprehensible on its own, without having to dig backwards to figure out what a variable really is.

            • You'll want and instead of & here. & is bitwise AND.






            share|improve this answer









            $endgroup$


















              0












              $begingroup$


              • Running this through flake8 and pycodestyle would give you some tips, such as variable naming conventions and spacing.

              • Names like pd and GDP_df are really unhelpful. Each line should be comprehensible on its own, without having to dig backwards to figure out what a variable really is.

              • You'll want and instead of & here. & is bitwise AND.






              share|improve this answer









              $endgroup$
















                0












                0








                0





                $begingroup$


                • Running this through flake8 and pycodestyle would give you some tips, such as variable naming conventions and spacing.

                • Names like pd and GDP_df are really unhelpful. Each line should be comprehensible on its own, without having to dig backwards to figure out what a variable really is.

                • You'll want and instead of & here. & is bitwise AND.






                share|improve this answer









                $endgroup$




                • Running this through flake8 and pycodestyle would give you some tips, such as variable naming conventions and spacing.

                • Names like pd and GDP_df are really unhelpful. Each line should be comprehensible on its own, without having to dig backwards to figure out what a variable really is.

                • You'll want and instead of & here. & is bitwise AND.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 10 mins ago









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                    Jeremy Hadfield is a new contributor. Be nice, and check out our Code of Conduct.










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