Creating Arrays from Scratch Especially for larger arrays, it is more efficient to create arrays from scratch using rou‐ tines built into NumPy. Here are several examples:

SeveralArray.py

In[1]: L = list(range(10))
           L
    Out[1]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    In[2]: type(L[0])
Out[2]: int

In[3]: L2 = [str(c) for c in L] L2
    Out[3]: ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
    In[4]: type(L2[0])
Out[4]: str

In[5]: L3 = [True, "2", 3.0, 4] [type(item) for item in L3]
    Out[5]: [bool, str, float, int]

In[6]: import array
L = list(range(10))
           A = array.array('i', L)
           A
    Out[6]: array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In[8]: # integer array:
           np.array([1, 4, 2, 5, 3])
    Out[8]: array([1, 4, 2, 5, 3])

  In[9]: np.array([3.14, 4, 2, 3])
Out[9]: array([ 3.14, 4. , 2. , 3. ])

  In[10]: np.array([1, 2, 3, 4], dtype='float32')
    Out[10]: array([ 1.,  2.,  3.,  4.], dtype=float32)

In[11]: # nested lists result in multidimensional arrays np.array([range(i, i + 3) for i in [2, 4, 6]])
    Out[11]: array([[2, 3, 4],
                    [4, 5, 6],
[6, 7, 8]])

In[12]: # Create a length-10 integer array filled with zeros np.zeros(10, dtype=int)
    Out[12]: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
In[13]: # Create a 3x5 floating-point array filled with 1s np.ones((3, 5), dtype=float)
    Out[13]: array([[ 1.,  1.,  1.,  1.,  1.],
                    [ 1.,  1.,  1.,  1.,  1.],
                    [ 1.,  1.,  1.,  1.,  1.]])
In[14]: # Create a 3x5 array filled with 3.14 np.full((3, 5), 3.14)

Out[14]: array([[ 3.14,  3.14,  3.14,  3.14,  3.14],
                [ 3.14,  3.14,  3.14,  3.14,  3.14],
                [ 3.14,  3.14,  3.14,  3.14,  3.14]])
In[15]: # Create an array filled with a linear sequence
# Starting at 0, ending at 20, stepping by 2
# (this is similar to the built-in range() function) np.arange(0, 20, 2)
Out[15]: array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])
In[16]: # Create an array of five values evenly spaced between 0 and 1 np.linspace(0, 1, 5)
Out[16]: array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])
In[17]: # Create a 3x3 array of uniformly distributed # random values between 0 and 1
        np.random.random((3, 3))
Out[17]: array([[ 0.99844933,  0.52183819,  0.22421193],
                [ 0.08007488,  0.45429293,  0.20941444],
                [ 0.14360941,  0.96910973,  0.946117  ]])
In[18]: # Create a 3x3 array of normally distributed random values # with mean 0 and standard deviation 1 np.random.normal(0, 1, (3, 3))
Out[18]: array([[ 1.51772646,  0.39614948, -0.10634696],
                [ 0.25671348,  0.00732722,  0.37783601],
                [ 0.68446945,  0.15926039, -0.70744073]])
In[19]: # Create a 3x3 array of random integers in the interval [0, 10) np.random.randint(0, 10, (3, 3))
Out[19]: array([[2, 3, 4],
                [5, 7, 8],
[0, 5, 0]])
In[20]: # Create a 3x3 identity matrix np.eye(3)
Out[20]: array([[ 1.,  0.,  0.],
                [ 0.,  1.,  0.],
                [ 0.,  0.,  1.]])
In[21]: # Create an uninitialized array of three integers
# The values will be whatever happens to already exist at that # memory location
np.empty(3)
Out[21]: array([ 1.,  1.,  1.])