Calculating the value of mathematical expression from the line

  in [ 6 ]:  import  numexpr  as  nein [ 7 ]: ne.evaluate ( '' 1/3+2/3 ') out [ 7 ]: array ( 1.0 ) in [ 8 ]: var1 =  10  in [ 9 ]: var2 =  2  in [ 10 ]: ne.evaluate ( 'Var1 ** Var2' ) out [ 10 ]: array ( 100 , dtype = int32)     
 he, by the way, is faster for more quickly complex calculations, supports the use of variables, supports Numpy, Scipy, etc. 
  
 maintains multi -flow calculations (using all available cores of the processor) and the  VML from Intel (Vector Math Library, which is integrated into the Intel Math Keel Library (Mkl)) . 
  
 An example of working with the usual ("Vanilla python" ") with an array: 
  
  in [ 45 ]: lst = [ 1 ,  2.718281828 ] in [ 46 ]: ne.evaluate ( 'log (lst)' ) out [ 46 ]: array ([ 0. ,  1. ])     
 with the Numpy Massion: 
  
  in [ 50 ]: a = np.array ([ 1 ,  2.718281828 ]) in [ 51 ]: ne.evaluate ( 'log (a)' ) out [ 51 ]: array ([ 0. ,  1. ])     
 comparison of productivity with Numpy for an array consisting of the 1st million elements of the type  numpy.float64 : 
  
  in [ 36 ]: a = np.random.rand ( 10  **  6 ) in [ 37 ]: a.shapeout [ 37 ]: ( 1000000 ,) in [ 38 ]:  len  (a) out [ 38 ]:  1000000  in [ 39 ]: %timeit np.log (a)   10  loops, Best of  3 :  24.3  ms per loopin [ 40 ]: %timeyit ne.evaluate ( 'log (a)' )   100  loops, Best of  3 :  5.45  ms per per Loopin [ 41 ]: %timeit np.sqrt (np.sin (a) **  2  + np.cos (a) **  2 )   10  loops, Best of  3 :  84.5  ms per loopin [ 42 ]: %Timeit ne.evaluate ( 'sqrt (sin (a) ** 2 + cos (a) ** 2)' )   100  loops, Best of  3 :  6.32  MS per loop