Creating Array ()

Numpy

Different methods to create Numpy array.

Creating array

import numpy as np
ar=np.array([1,5,2,8,3])
print(ar) # [1 5 2 8 3]

Creating two dimensional array

ar=np.array([[1,5,2,8,3],[8,5,3,11,1]])
print(ar)
Output
[[ 1  5  2  8  3]
 [ 8  5  3 11  1]]
We can use shape to get the rows and columns of the array. In above code we can check size of the arrays.

For the two dimensional array we will get this output.
print(ar.shape) # (3, 4)
There are 3 rows and 4 columns in above array. You can try shape for other arrays.

Creating arrays using ones() and eye()

Eye array of shape(3,4) Read more on eye() here.

We can create array by using eye(). We will get an array by filling 1 at diagonal positions.

Let us create one array by (3,4) shape.
ar=np.eye(3,4)
print(ar)
Output
[[1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]]

ones()

Ones array of shape(3,4) We can create array of any shape and fill it by 1s by using ones().
ar=np.ones((4,3))
print(ar)
Output
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Creating array using arange()

Using arange() we can create array with evenly spaced values
print(np.arange(1,5,dtype=np.int8)) # [1 2 3 4]

Creating array using linspace

Using linespace() we can create array with given number of elements
print(np.linspace(2,10,5)) # [ 2.  4.  6.  8. 10.]

Aray with random numbers

We can create array with random numbers
my_data=np.random.rand(4)
print(my_data)
Output
[0.3630618  0.76575666 0.36043609 0.69937788]

Array with zeros()

We can create array filled with zeros by using zeros()
ar=np.zeros(4,dtype=int)
print(ar) # [0 0 0 0]

Using np.full()

We can create array by filling any data by using full()
ar=np.full((1,3),5)
print(ar) # [[5 5 5]]

Creating empty array

We can create array without initializing entries.
ar=np.empty((1,3))
print(ar)
Output
[[5.e-324 5.e-324 5.e-324]]

Creating Multidimensional array

We will be creating multidimensional arrays by specifying ndim value.
ar = np.array([1, 2, 3, 4,5,6], ndmin=2)
print(ar)
print('Shape :', ar.shape)
print('Dimension :',ar.ndim)
Output
[[1 2 3 4 5 6]]
Shape : (1, 6)
Dimension : 2
shape() : Shape and dimension of the input array

Numpy ones() bincount() arange() linspace()


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