numpy basics python

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

• a powerful N-dimensional array object

• sophisticated (broadcasting) functions

• tools for integrating C/C++ and Fortran code

• useful linear algebra, Fourier transform, and random number capabilities

Importing Numpy:

import numpy as np

Creating numpy Array:

 d = np.array([1,2,3,4,5])

Numpy range:

  d = np.arange(1,10). # It will create numpy array from range 1 to 9

   

numpy shape:

It will return total elements count based on rows or shape

d = np.array([1,2,3])

print d   # array([1, 2, 3])

print d.shape # (3,)

numpy reshape:

It will change the shape of numpy arrays

d = np.arange(1,10)    # array([1,2,3,4,5,6,7,8,9])

d.shape     # (9,)

d.reshape(3,3)

print d    #  Array([[1, 2, 3],

                                [4, 5, 6],

                                [7, 8, 9]])

Above example, it will reshape like 3X3 matrix structure

np.zeros()

    It will create zero value matrix numpy array. We have to give dimension value in the function and it will create matrix arrays.

np.zeros(3, 3)       # Array([[0., 0., 0.],

                                            [0., 0., 0.],

                                            [0., 0., 0.]]) 

np.vstack()

It will vertically stack each elements in  numpy array.

c = np.array([1,2,3])  # array([1, 2, 3])

np.vstack(c)    # array([ [ 1],

                                       [ 2],

                                       [ 3]])

   

np.eye()

It will create numpy  identical matrix array.

 np.eye(3) # it will create 3X3 matrix


                     Array (  [ 1,    0,   0]

                                   [ 0,     1,   1]

                                   [ 0,     0,   1])

        

     
np.dot()

 It will dot product of two matrix (multiplication)       

 np.dot(M1, M2)

np.sum()

 It will sum of all the elements in given array.

#M = Array([[1, 2, 3],

            [4, 5, 6],

            [7, 8, 9]])

np.sum(M) # 45 it will sum all the elements in array

np.sum(M, axis=0)   # [[12, 15, 18]] 

    

If axis= 0, it will sum column wise, it 

If axis = 1, it will sum row wise

np.random.rand()

It will produce random np arrays

np.append()

Append elements to nd array

 A = array([1, 2, 3])

 B = np.append(A, 4) # [1, 2, 3, 4]

 B = np.append(A, [4, 5,6,7]) # [1, 2, 3, 4, 5, 6, 7]