NumPy Introduction

NumPy(Numerical Python) is a fundamental library for Python numerical computing. It provides efficient multi-dimensional array objects and various mathematical functions for handling large datasets making it a critical tool for professionals in fields that require heavy computation.

Key Features of NumPy

NumPy has various features that make it popular over lists.

• N-Dimensional Arrays: NumPy's core feature is ndarray, a N-dimensional array object that supports homogeneous data types.
• Arrays with High Performance: Arrays are stored in contiguous memory locations, enabling faster computations than Python lists (Please see Numpy Array vs Python List for details).
• Broadcasting: This allows element-wise computations between arrays of different shapes. It simplifies operations on arrays of various shapes by automatically aligning their dimensions without creating new data.
• Vectorization: Eliminates the need for explicit Python loops by applying operations directly on entire arrays.
• Linear algebra: NumPy contains routines for linear algebra operations, such as matrix multiplication, decompositions, and determinants.

Installing NumPy in Python

To begin using NumPy, you need to install it first. This can be done using the following pip command:

pip install numpy

Once installed, import the library with the alias np

import numpy as np

Creating NumPy Arrays

1. Using np.array: Use np.array() when you want to convert Python lists into NumPy arrays.

import numpy as np a1 = np.array([1, 2, 3]) # 1D array a2 = np.array([[1, 2], [3, 4]]) # 2D array a3 = np.array([[[1, 2], [3, 4]], # 3D array [[5, 6], [7, 8]]]) print(a1) print(a2) print(a3) 

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

2. Using Numpy Functions: NumPy provides quick utility functions for creating arrays filled with zeros, ones, or ranges:

import numpy as np a0 = np.zeros((3, 3)) a1 = np.ones((2, 2)) ar = np.arange(0, 10, 2) print(a0) print(a1) print(ar) 

[[0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[1. 1.] [1. 1.]] [0 2 4 6 8] 

NumPy Array Indexing

Advanced indexing in NumPy uses arrays of integers or boolean masks to extract complex patterns of elements, enabling non-contiguous and condition-based selection.

import numpy as np a1 = np.array([10, 20, 30, 40, 50]) print(a1[2]) # single element print(a1[-1]) # last element a2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) print(a2[1, 0]) # row 1, column 0 

30 50 4 

NumPy Array Slicing

Slicing in NumPy follows the same indexing rules as Python lists, but extends them to multiple dimensions, allowing you to select rows, columns, or sub-arrays efficiently.

import numpy as np a = np.array([[1, 2, 3], [4, 5, 6]]) print(a[1:4]) # row slice print(a[:, 1]) # all rows, column 1 

[[4 5 6]] [2 5] 

Advanced Indexing

Advanced Indexing in NumPy provides more flexible ways to access and manipulate array elements.

import numpy as np a = np.array([10, 20, 30, 40, 50, 60]) idx = np.array([1, 3, 5]) print(a[idx]) # integer indexing cond = a > 30 print(a[cond]) # boolean indexing 

[20 40 60] [40 50 60] 

NumPy Basic Arithmetic Operations

Element-wise operations in NumPy allow you to perform mathematical operations on each element of an array individually, without the need for explicit loops. We can perform arithmetic operations like addition, subtraction, multiplication, and division directly on NumPy arrays.

import numpy as np x = np.array([1, 2, 3]) y = np.array([4, 5, 6]) print(x + y) # add print(x - y) # subtract print(x * y) # multiply print(x / y) # divide 

[5 7 9] [-3 -3 -3] [ 4 10 18] [0.25 0.4 0.5 ] 

Unary Operation

Unary operations in NumPy apply a single-operand transformation-such as negation, absolute value, or trigonometric evaluation-across entire arrays efficiently without the need for multiple arrays (as in binary operations).

import numpy as np a = np.array([-3, -1, 0, 1, 3]) # array with both positive and negative values # Applying a unary operation: absolute value print(np.absolute(a)) 

[3 1 0 1 3] 

Binary Operators

Numpy Binary Operations apply to the array elementwise and a new array is created. We can use all basic arithmetic operators like +, -, /,  etc. In the case of +=, -=, = operators, the existing array is modified.

import numpy as np # Two example arrays a1 = np.array([1, 2, 3]) a2 = np.array([4, 5, 6]) # Applying a binary operation: addition res = np.add(a1, a2) print(res) 

[5 7 9] 

NumPy Mathematical Functions

NumPy provides familiar mathematical functions such as sin, cos, exp, etc. These functions also operate elementwise on an array, producing an array as output.

import numpy as np # create an array of sine values a = np.array([0, np.pi/2, np.pi]) print(np.sin(a)) # exponential values b = np.array([0, 1, 2, 3]) print(np.exp(b)) print(np.sqrt(b)) # square root of array values print (np.sqrt(a)) 

[0.0000000e+00 1.0000000e+00 1.2246468e-16] [ 1. 2.71828183 7.3890561 20.08553692] [0. 1. 1.41421356 1.73205081] [0. 1.25331414 1.77245385] 

NumPy Sorting Arrays

We can use a simple np.sort() method for sorting Python NumPy arrays.

import numpy as np dtype = [('name', 'S10'), ('year', int), ('cgpa', float)] vals = [('Hrithik', 2009, 8.5), ('Ajay', 2008, 8.7), ('Pankaj', 2008, 7.9), ('Aakash', 2009, 9.0)] a = np.array(vals, dtype=dtype) print(np.sort(a, order='name')) print(np.sort(a, order=['year', 'cgpa'])) 

[(b'Aakash', 2009, 9. ) (b'Ajay', 2008, 8.7) (b'Hrithik', 2009, 8.5) (b'Pankaj', 2008, 7.9)] [(b'Pankaj', 2008, 7.9) (b'Ajay', 2008, 8.7) (b'Hrithik', 2009, 8.5) (b'Aakash', 2009, 9. )] 

Related Article

• Different ways to create numpy arrays
• Numpy Functions
• Python Libraries
• NumPy Tutorial
• Python Numpy
• Broadcasting