

In [1]:

    
import numpy as np

ndarray



In [7]:

    
array = np.arange(15)
shape = array.reshape(3, 5)
shape









    Out[7]:





array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

ndarray.shape: the dimension of the array



In [8]:

    
shape.shape









    Out[8]:





(3, 5)

ndarray.ndim: the number of axes (dimensions) of the array



In [9]:

    
shape.ndim









    Out[9]:





2

Array Creation



In [12]:

    
np.array([2,3,4])









    Out[12]:





array([2, 3, 4])



In [11]:

    
np.array([(1.5,2,3), (4,5,6)])









    Out[11]:





array([[ 1.5,  2. ,  3. ],
       [ 4. ,  5. ,  6. ]])



In [13]:

    
np.zeros( (3,4) )









    Out[13]:





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



In [14]:

    
np.ones( (2,3,4), dtype=np.int16 )









    Out[14]:





array([[[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]],

       [[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]]], dtype=int16)



In [15]:

    
np.arange( 10, 30, 5 ) # a sequence of number: start, end, step









    Out[15]:





array([10, 15, 20, 25])



In [17]:

    
np.arange( 0, 2, 0.3 ) # float









    Out[17]:





array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8])



In [16]:

    
np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2









    Out[16]:





array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ,  1.25,  1.5 ,  1.75,  2.  ])



In [ ]: