Numpy-

  • – Numpy means numerical python.
  • – High performance linear algebra and matrix related library.
  • – It is written in Python.
  • – Majorly used in 1D and multidirectional data manipulation.

We Can Install Numpy Library By Two Ways-

  • 1) pip install numpy
  • 2) conda install numpy
import numpy as np
list1 = [1,2,3]
np.array(list1)
Output-array([1, 2, 3])

np.array([4,5,6])
Output-array([4,5,6])

np.array([list1, list1])
Output-array([[1, 2, 3],
              [1, 2, 3]])
			  

# To create an array of element 'one' having row=3 and column=4	  
np.ones(shape=(3,4)) #touple will be used to define the array's row and column values
Output-array([[1., 1., 1., 1.],
			  [1., 1., 1., 1.],
			  [1., 1., 1., 1.]])


7*np.ones(shape=(3,4))
Output-
array([[7., 7., 7., 7.],
       [7., 7., 7., 7.],
       [7., 7., 7., 7.]])

	   
# To create an array of element zero having row=3 and column=4	  
np.zeros(shape=(3,4)) #touple will be used to define the array's row and column values
Output-array([[0., 0., 0., 0.],
			  [0., 0., 0., 0.],
              [0., 0., 0., 0.]])
			  
			 
# start, stop, step			 
np.arange(7)
Output-array([0, 1, 2, 3, 4, 5, 6])

np.arange(3,7)
Output-array([3, 4, 5, 6])


np.arange(10,1,-2)
Output-array([10,  8,  6,  4,  2])

#How to generate an array of random no.
np.random.randn(2,3)
Output-array([[-1.43932592,  0.03042645,  0.50291257],
       [ 0.33814608,  1.54829719,  0.82410219]])


#How to generate an integer type random no.
np.random.randint(1,7) #(start_range, end_range, no_of_times_frequency)
Output-3


np.random.randint(1,7,3)
Output-array([4, 6, 6])

my_array = np.random.randn(3,4)
my_array
Output-array([[ 0.89226386, -0.73460617,  0.75015457, -0.66980253],
       [ 1.77165398,  0.73811318,  1.31317468, -1.1242388 ],
       [-0.63671896, -0.76357297, -1.34281154,  0.19971696]])
	   
my_array.size
12	 	

#We can change its shape but size should be same as previous one.
my_array.reshape(2,6) # This is allowed as size is same as 12.
array([[ 0.89226386, -0.73460617,  0.75015457, -0.66980253,  1.77165398,
         0.73811318],
       [ 1.31317468, -1.1242388 , -0.63671896, -0.76357297, -1.34281154,
         0.19971696]])


my_array.reshape(3,6)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
 in 
----> 1 my_array.reshape(3,6)

ValueError: cannot reshape array of size 12 into shape (3,6)

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

a[1] #Array will start from index 0
2

arr[star_index : end_index]
a[2:7]
array([3, 4, 5, 6, 7])

a[2:] # We didn't provide end index so it will print till end of the array
array([3, 4, 5, 6, 7, 8, 9])

#We didn't provide start index so it will print from begining of the array till the index provided by us.
a[:7]
array([1, 2, 3, 4, 5, 6, 7])

# There is no start and end index provided so it will print from 0 index to end of the array
a[:] 
array([1, 2, 3, 4, 5, 6, 7, 8, 9])


a[-1] # This is slicing so index -1 start from end
9

a[-2]
8
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