NumPy ndarray

NumPy ndarray stands for n-dimensional array. This is a collection of homogenous “items” of the same “kinds”. The kind can be arbitrary data structure and is defined using data types.

Create an Array

We can import the package by using the import command and create an array,

import numpy 
arr=numpy.array([[1,2,3,4],[5,6,7,8]],float) 
print(arr) 

# [[1. 2. 3. 4.] 
# [5. 6. 7. 8.]] 

We can use an alias to do the same, as 

import numpy as np 
arr=np.array([[1,2,3,4],[5,6,7,8]],float) 
print(arr) 

We can check the NumPy version, 

import numpy as np 
print(np.__version__) 

Getting the type

import numpy as np 
arr=np.array([[1,2,3,4],[5,6,7,8]],float) 
print(type(arr)) 

#Output
#<class 'numpy.ndarray'>

Getting the shape and size of an array

import numpy as np 
arr=np.array([[1,2,3,4],[5,6,7,8]],float) 
print('Array shape {} and size {}'.format(arr.shape, arr.itemsize)) 

#Array shape (2, 4) and size 8 

0-D array

A 0-D array is a scaler. Each value itself is an 0-D array in a multi-dimensional array. For example,

import numpy as np
arr = np.array(45.8)
print(arr)

1-D array

An array which is the collection of all scalers is a 1-D array. This is a unidirectional array.

import numpy as n
arr = n.array(['Python','Java','PHP','JavaScript'])
print(arr)

#['Python' 'Java' 'PHP' 'JavaScript']

2-D array

An array that has 1-D arrays as its elements is known as a 2-D array. A 2-D array is used to represent matrix or second-order tensors. NumPy provides a specially dedicated submodule NumPy.mat for these arrays.

import numpy 
arr=numpy.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]]) 
print(arr) 

#[[ 1  2  3  4]
#[ 5  6  7  8]
#[ 9 10 11 12]]

3-D array

A 3-D array is a collection of 2-D arrays as its elements. 3-D arrays can be used to represent 3rd order tensors. For example,

import numpy as np
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(arr)

#[[[10 12 13]
#  [ 6  7 11]
#  [ 4  2  7]]

# [[ 0  9  7]
#  [ 4 15  2]
#  [ 9  6  3]]]
 

Getting and setting the dimension

import numpy as npy
arr = npy.array([[[10, 12, 13], [6, 7, 11],[4,2,7]], [[0, 9, 7], [4, 15, 2],[9,6,3]]])
print(arr.ndim)  # 3 Get the dim

#We can also declare the number of dim
#at the time of creation
arnew=npy.array(['a','e','i','o','u'],ndmin=3)
print(arnew.ndim) #3 Setting minimum dimension