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In [2]:

    
from pandas import *



In [3]:

    
df = read_csv("pandas_different_indices.txt",sep=r'\s+',names=['DATE','GDP','USRECQ'],index_col=0,parse_dates=True)



In [4]:

    
df

I would like to count the number of observation for which USRECQ[DATE+1]==1 and GDP[DATE]>a if GDP[DATE]!='NAN'.



In [30]:

    
df[np.logical_and(df.shift(-1)['USRECQ'] == 1,df.GDP > 0)]



In [33]:

    
df[np.logical_and(df.shift(-1)['USRECQ'] == 1,df.GDP > 0.07)]



In [35]:

    
df[np.logical_and(df.shift(-1)['USRECQ'] == 1,df.GDP < 0.04)]

So we can take the len of any of these expressions to get a count



In [36]:

    
len(df[np.logical_and(df.shift(-1)['USRECQ'] == 1,df.GDP < 0.04)])









    Out[36]:





1



In [37]:

    
len(df[np.logical_and(df.shift(-1)['USRECQ'] == 1,df.GDP > 0)])









    Out[37]:





2



In [39]:

    
def count_by_gdp(df,gdp):
    ''' Count entries where the next entry has USRECQ == 1 and the entry's GDP
        is > gdp
    '''
    return len(df[np.logical_and(df.shift(-1)['USRECQ'] == 1,df.GDP > gdp)])



In [40]:

    
count_by_gdp(df,0.03)









    Out[40]:





2



In [41]:

    
count_by_gdp(df,0.07)









    Out[41]:





1



In [43]:

    
count_by_gdp(df,-0.01)









    Out[43]:





2



In [ ]:

	GDP	USRECQ
DATE
1947-01-01	NaN	0
1947-04-01	NaN	0
1947-07-01	NaN	0
1947-10-01	NaN	0
1948-01-01	0.095023	0
1948-04-01	0.107998	0
1948-07-01	0.117553	0
1948-10-01	0.078371	0
1949-01-01	0.034560	1
1949-04-01	-0.004397	1

	GDP	USRECQ
DATE
1948-10-01	0.078371	0
1949-01-01	0.034560	1