In [1]:

    

#"NumPy's main object is the homogeneous multidimensional array"
import numpy as np

arr = np.array([[0,1,2],[3,4,5]])

arr


    

    
Out[1]:





array([[0, 1, 2],
       [3, 4, 5]])

In [2]:

    

#Array class (also known as array but np.array is not the same as standard python's array.array)
type(arr)


    

    
Out[2]:





numpy.ndarray

In [3]:

    

#Number of axes (also known as rank)
arr.ndim


    

    
Out[3]:





2

In [4]:

    

#Dimensions of the array (n rows by m columns)
arr.shape


    

    
Out[4]:





(2, 3)

In [5]:

    

#Total number of elements (product of shape)
arr.size


    

    
Out[5]:





6

In [6]:

    

#Type of elements in the array (inferred if not specified)
arr.dtype


    

    
Out[6]:





dtype('int32')

In [7]:

    

#Array from nested lists
arr = np.array([[1,2,3],[4,5,6]]) 

arr


    

    
Out[7]:





array([[1, 2, 3],
       [4, 5, 6]])

In [8]:

    

#Array of zeros
np.zeros((2,3))


    

    
Out[8]:





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

In [9]:

    

#Array of ones
np.ones((2,3))


    

    
Out[9]:





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

In [10]:

    

#"Empty" array
np.empty((2,3))


    

    
Out[10]:





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

In [11]:

    

#Sequence of numbers - similar to range
np.arange(0,50,10) #Start, stop, step


    

    
Out[11]:





array([ 0, 10, 20, 30, 40])

In [12]:

    

#Sequence of numbers - specific number of elements specified
np.linspace(0,50,5) #Start, stop, number of elements


    

    
Out[12]:





array([  0. ,  12.5,  25. ,  37.5,  50. ])

In [13]:

    

#Sequence of numbers - with specific shape
np.arange(6).reshape(2,3)


    

    
Out[13]:





array([[0, 1, 2],
       [3, 4, 5]])

In [14]:

    

a = np.arange(0,100,10)
b = np.arange(10)


    

In [15]:

    

a


    

    
Out[15]:





array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])

In [16]:

    

b


    

    
Out[16]:





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

In [17]:

    

#Element-wise subtraction
a-b


    

    
Out[17]:





array([ 0,  9, 18, 27, 36, 45, 54, 63, 72, 81])

In [18]:

    

#Element-wise addition
a+b


    

    
Out[18]:





array([ 0, 11, 22, 33, 44, 55, 66, 77, 88, 99])

In [19]:

    

#Matrix multiplication
np.dot(a,b)


    

    
Out[19]:





2850

In [20]:

    

#Matrix multiplication (option 2)
a.dot(b)


    

    
Out[20]:





2850

In [21]:

    

arr = np.arange(12).reshape(3,4)
arr


    

    
Out[21]:





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

In [22]:

    

#Sum of all elements (as if a flat list)
arr.sum()


    

    
Out[22]:





66

In [23]:

    

#Min of all elements (as if a flat list)
a.min()


    

    
Out[23]:





0

In [24]:

    

#Max of all ements (as if a flat list)
a.max()


    

    
Out[24]:





90

In [25]:

    

#Column sum (axis=1 for row sum)
arr.sum(axis=0)


    

    
Out[25]:





array([12, 15, 18, 21])

In [26]:

    

#Column min (axis=1 for row min)
arr.sum(axis=0)


    

    
Out[26]:





array([12, 15, 18, 21])

In [27]:

    

#Culumulative sum along column (axis=1 for row)
arr.cumsum(axis=0)


    

    
Out[27]:





array([[ 0,  1,  2,  3],
       [ 4,  6,  8, 10],
       [12, 15, 18, 21]], dtype=int32)

In [28]:

    

#Exponentiation
arr = np.arange(3)
np.exp(arr)


    

    
Out[28]:





array([ 1.        ,  2.71828183,  7.3890561 ])

In [29]:

    

#Square roote
np.sqrt(arr)


    

    
Out[29]:





array([ 0.        ,  1.        ,  1.41421356])