NumPy Introduction (original) (raw)

Last Updated : 28 Jan, 2025

**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.

Table of Content

Key Features of NumPy
Installing NumPy in Python
Creating NumPy Arrays
NumPy Array Indexing
NumPy Basic Operations
NumPy ufuncs
NumPy Sorting Arrays

Key Features of NumPy

NumPy has various features that make it popular over lists.

**N-Dimensional Arrays: NumPy’s core feature is ndarray, a powerful 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 through pip command:

**pip install numpy

Once installed, import the library with the alias np

import numpy as np

Creating NumPy Arrays

**Using ndarray : The array object is called ndarray. NumPy arrays are created using the array() function.

**Example:

Python `

import numpy as np

Creating a 1D array

x = np.array([1, 2, 3])

Creating a 2D array

y = np.array([[1, 2], [3, 4]])

Creating a 3D array

z = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

print(x) print(y) print(z)

Output

[1 2 3] [[1 2] [3 4]] [[[1 2] [3 4]]

[[5 6] [7 8]]]

**Using Numpy Functions: NumPy provides convenient methods to create arrays initialized with specific values like zeros and ones:

**Example:

Python `

import numpy as np

a1_zeros = np.zeros((3, 3)) a2_ones = np.ones((2, 2)) a3_range = np.arange(0, 10, 2)

print(a1_zeros) print(a2_ones) print(a3_range)

Output

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

You can also refer to this article – Different ways to create numpy arrays

**NumPy Array Indexing

Knowing the basics of NumPy array indexing is important for analyzing and manipulating the array object.

**Basic Indexing: Basic indexing in NumPy allows you to access elements of an array using indices.

**Example:

Python `

import numpy as np

Create a 1D array

arr1d = np.array([10, 20, 30, 40, 50])

Single element access

print("Single element access:", arr1d[2])

Negative indexing

print("Negative indexing:", arr1d[-1])

Create a 2D array

arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

Multidimensional array access

print("Multidimensional array access:", arr2d[1, 0])

Output

Single element access: 30 Negative indexing: 50 Multidimensional array access: 4

**Slicing: Just like lists in Python, NumPy arrays can be sliced. As arrays can be multidimensional, you need to specify a slice for each dimension of the array.

**Example:

Python `

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]]) #elements from index 1 to 3 print("Range of Elements:",arr[1:4])

#all rows, second column print("Multidimensional Slicing:", arr[:, 1])

Output

Range of Elements: [[4 5 6]] Multidimensional Slicing: [2 5]

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

**Example:

Python `

import numpy as np arr = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])

Integer array indexing

indices = np.array([1, 3, 5]) print ("Integer array indexing:", arr[indices])

boolean array indexing

cond = arr > 0 print ("\nElements greater than 0:\n", arr[cond])

Output

Elements at indices (0, 3), (1, 2), (2, 1),(3, 0): [4. 6. 0. 3.]

Elements greater than 0: [2. 4. 4. 6. 2.6 7. 8. 3. 4. 2. ]

**NumPy Basic 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.

**Element-wise Operations: We can perform arithmetic operations like addition, subtraction, multiplication, and division directly on NumPy arrays.

**Example:

Python `

import numpy as np

x = np.array([1, 2, 3]) y = np.array([4, 5, 6])

Addition

add = x + y
print("Addition:",add)

Subtraction

subtract = x - y print("substration:",subtract)

Multiplication

multiply = x * y print("multiplication:",multiply)

Division

divide = x / y
print("division:", divide)

Output

Addition: [5 7 9] substration: [-3 -3 -3] multiplication: [ 4 10 18] division: [0.25 0.4 0.5 ]

**Unary Operation: These operations are applied to each individual element in the array, without the need for multiple arrays (as in binary operations).

**Example:

Python `

import numpy as np

Example array with both positive and negative values

arr = np.array([-3, -1, 0, 1, 3])

Applying a unary operation: absolute value

result = np.absolute(arr) print("Absolute value:", result)

Output

Absolute value: [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.

**Example:

Python `

import numpy as np

Two example arrays

arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6])

Applying a binary operation: addition

result = np.add(arr1, arr2)

print("Array 1:", arr1) print("Array 2:", arr2) print("Addition Result:", result)

Output

Array 1: [1 2 3] Array 2: [4 5 6] Addition Result: [5 7 9]

**NumPy ufuncs

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

**Example:

Python `

import numpy as np

create an array of sine values

a = np.array([0, np.pi/2, np.pi]) print ("Sine values of array elements:", np.sin(a))

exponential values

a = np.array([0, 1, 2, 3]) print ("Exponent of array elements:", np.exp(a))

square root of array values

print ("Square root of array elements:", np.sqrt(a))

**Output:

Sine values of array elements: [ 0.00000000e+00 1.00000000e+00 1.22464680e-16] Exponent of array elements: [ 1. 2.71828183 7.3890561 20.08553692] Square root of array elements: [ 0. 1. 1.41421356 1.73205081]

**NumPy Sorting Arrays

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

**Example:

Python `

import numpy as np

set alias names for dtypes

dtypes = [('name', 'S10'), ('grad_year', int), ('cgpa', float)]

Values to be put in array

values = [('Hrithik', 2009, 8.5), ('Ajay', 2008, 8.7), ('Pankaj', 2008, 7.9), ('Aakash', 2009, 9.0)]

Creating array

arr = np.array(values, dtype = dtypes) print ("\nArray sorted by names:\n", np.sort(arr, order = 'name'))

print ("Array sorted by graduation year and then cgpa:\n", np.sort(arr, order = ['grad_year', 'cgpa']))

Output

Array sorted by names: [(b'Aakash', 2009, 9. ) (b'Ajay', 2008, 8.7) (b'Hrithik', 2009, 8.5) (b'Pankaj', 2008, 7.9)] Array sorted by graduation year and then cgpa: [(b'Pankaj', 2008, 7.9) (b'Ajay',...

**Read More: