Create numpy array¶

Create numpy array from data¶

Single Dimensional Array

In [84]:

x1  = (100,200,300,400,500)
x1n  = np.array(x1)
x1n1 = np.array(x1, 'int')
x1n2 = np.array(x1, 'float')
x1n3 = np.array(x1, 'str')
print ('x1n3:\n ', x1n3, 'data type: ', type(x1n3))

x1n3:
  ['100' '200' '300' '400' '500'] data type:  <class 'numpy.ndarray'>

Multi-Dimensional Array

In [85]:

x2 = ((1,2,3,4,5), (11,12,13,14,15),(21,22,23,24,25))
x2n = np.array(x2)
x2n1 = np.array(x2,'int')
x2n2 = np.array(x2,'float')
x2n3 = np.array(x2,'str')
print ('x2n3:\n',x2n3)

x2n3:
 [['1' '2' '3' '4' '5']
 ['11' '12' '13' '14' '15']
 ['21' '22' '23' '24' '25']]

Auto generate numpy array¶

Using numpy.arange(), return np.array of integer

In [86]:

np.arange(10)

Out[86]:

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

Using range(), return np.array of integer

In [87]:

np.array(range(10))

Out[87]:

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

Using both numpy.arange() and range()

In [88]:

np.array([range(10),np.arange(10)])

Out[88]:

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

Using numpy.ones(), return can specify int, float or str

In [89]:

np.ones(10,'int')

Out[89]:

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

Create Identity Matrix using np.identiyt(n), n is dimension

In [90]:

np.identity(5)

Out[90]:

array([[ 1.,  0.,  0.,  0.,  0.],
       [ 0.,  1.,  0.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  1.,  0.],
       [ 0.,  0.,  0.,  0.,  1.]])