- Numpy is a general-purpose array-processing package.
- It provides a high-performance multidimensional array object, and tools for working with these arrays.
- It is the fundamental package for scientific computing with Python.
- Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data.
Arrays in Numpy
- Array in Numpy is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers.
- In Numpy, number of dimensions of the array is called rank of the array.
- A tuple of integers giving the size of the array along each dimension is known as shape of the array.
- An array class in Numpy is called as ndarray.
- Elements in Numpy arrays are accessed by using square brackets and can be initialized by using nested Python Lists.
Creating a Numpy Array
- Arrays in Numpy can be created by multiple ways, with various number of Ranks, defining the size of the Array.
- Arrays can also be created with the use of various data types such as lists, tuples, etc.
- The type of the resultant array is deduced from the type of the elements in the sequences.
Note: Type of array can be explicitly defined while creating the array.
# Python program for
# Creation of Arrays
import numpy as np
# Creating a rank 1 Array
arr = np.array([1, 2, 3])
print("Array with Rank 1: \n",arr)
# Creating a rank 2 Array
arr = np.array([[1, 2, 3],[4, 5, 6]])
print("Array with Rank 2: \n", arr)
# Creating an array from tuple
arr = np.array((1, 3, 2))
print("\nArray created using "
"passed tuple:\n", arr)
Output:
Array with Rank 1:
[1 2 3]
Array with Rank 2:
[[1 2 3]
[4 5 6]]
Array created using passed tuple:
[1 3 2]
