Numpy

NumPy, short for Numerical Python, is the fundamental package required for high performance scientific computing and data analysis.

While NumPy by itself does not provide very much high-level data analytical func- tionality, having an understanding of NumPy arrays and array-oriented computing will help you use tools like pandas much more effectively.



In [2]:

    
import numpy



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dir(numpy)



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help(numpy.zeros)



In [5]:

    
a = numpy.zeros( (3,5) )



In [6]:

    
a









    Out[6]:





array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])



In [8]:

    
a[(2,2)] = 3
a









    Out[8]:





array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  3.,  0.,  0.]])



In [10]:

    
import numpy as np

1-D array



In [12]:

    
b = np.array( [2., 4., 6.])
b









    Out[12]:





array([ 2.,  4.,  6.])



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b = np.array( range(10) )
b









    Out[14]:





array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])



In [15]:

    
b = np.array( (2., 4., 6.) )
b









    Out[15]:





array([ 2.,  4.,  6.])

2-D array (matrix)



In [16]:

    
m = np.array( [(2., 3., 4.), (5., 6., 7.)])
m









    Out[16]:





array([[ 2.,  3.,  4.],
       [ 5.,  6.,  7.]])



In [18]:

    
m = np.array( [[2., 3., 4.], [5., 6., 7.]] )
m









    Out[18]:





array([[ 2.,  3.,  4.],
       [ 5.,  6.,  7.]])



In [21]:

    
m * 3









    Out[21]:





array([[  6.,   9.,  12.],
       [ 15.,  18.,  21.]])



In [20]:

    
m + m









    Out[20]:





array([[  4.,   6.,   8.],
       [ 10.,  12.,  14.]])



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