NumPy Tutorial – Learn Numerical Computing in Python

NumPy Tutorial

NumPy (Numerical Python) is one of the most important libraries in Python for scientific computing and numerical operations.

It provides:

• Fast multidimensional arrays
• Mathematical functions
• Statistical operations
• Linear algebra tools
• Random number generation
• Broadcasting capabilities

NumPy serves as the foundation for many popular Python libraries used in data science, machine learning, and artificial intelligence.

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What is NumPy?

NumPy is an open-source Python library designed for efficient numerical computations.

Instead of working with traditional Python lists, NumPy uses specialized array objects that provide:

• Better performance
• Lower memory usage
• Faster mathematical operations

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Why Use NumPy?

Faster Than Python Lists

NumPy arrays are implemented in optimized C code, making calculations significantly faster.

Less Memory Usage

Arrays consume less memory compared to Python lists.

Powerful Mathematical Functions

Provides built-in functions for:

• Statistics
• Algebra
• Matrix operations
• Scientific computing

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Installing NumPy

Install NumPy using pip:

pip install numpy

Verify installation:

import numpy as np

print(np.__version__)

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Importing NumPy

The standard convention is:

import numpy as np

Here, np is simply an alias for NumPy.

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Creating NumPy Arrays

One-Dimensional Array

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

print(arr)

Output:

[1 2 3 4 5]

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Two-Dimensional Array

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

print(arr)

Output:

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

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Three-Dimensional Array

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

print(arr)

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Array Attributes

Shape

Shows dimensions of an array.

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

print(arr.shape)

Output:

(2, 2)

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Number of Dimensions

print(arr.ndim)

Output:

2

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Data Type

print(arr.dtype)

Output:

int64

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Creating Special Arrays

Zeros Array

arr = np.zeros((3, 3))

Output:

[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

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Ones Array

arr = np.ones((2, 2))

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Identity Matrix

arr = np.eye(3)

Output:

[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]

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Range of Values

arr = np.arange(0, 10, 2)

Output:

[0 2 4 6 8]

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Array Indexing

arr = np.array([10, 20, 30])

print(arr[0])

Output:

10

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Array Slicing

arr = np.array([1, 2, 3, 4, 5])

print(arr[1:4])

Output:

[2 3 4]

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Mathematical Operations

Addition

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)

Output:

[5 7 9]

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Multiplication

print(a * b)

Output:

[ 4 10 18]

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Division

print(a / b)

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Statistical Functions

Sum

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

print(np.sum(arr))

Output:

10

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Mean

print(np.mean(arr))

Output:

2.5

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Median

print(np.median(arr))

Output:

2.5

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Standard Deviation

print(np.std(arr))

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Reshaping Arrays

arr = np.arange(12)

new_arr = arr.reshape(3, 4)

print(new_arr)

Output:

[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

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Broadcasting

Broadcasting allows NumPy to perform operations on arrays with different shapes.

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

print(arr + 10)

Output:

[11 12 13]

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Random Numbers

Random Float

import numpy as np

print(np.random.rand())

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Random Array

print(np.random.rand(3, 3))

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Random Integers

print(np.random.randint(1, 100, 5))

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Linear Algebra

Matrix Multiplication

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

b = np.array([[5, 6],
              [7, 8]])

print(np.dot(a, b))

Output:

[[19 22]
 [43 50]]

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Determinant

print(np.linalg.det(a))

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Inverse Matrix

print(np.linalg.inv(a))

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Real-World Example

Calculate average monthly sales:

import numpy as np

sales = np.array([
    1200,
    1500,
    1800,
    2000,
    2200
])

average = np.mean(sales)

print(average)

Output:

1740.0

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Common NumPy Functions

Function	Purpose
array()	Create array
zeros()	Create zeros
ones()	Create ones
arange()	Create sequence
reshape()	Change shape
sum()	Calculate sum
mean()	Calculate average
median()	Find median
std()	Standard deviation
dot()	Matrix multiplication

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Best Practices

• Use NumPy arrays instead of large Python lists.
• Utilize vectorized operations.
• Avoid unnecessary loops.
• Use broadcasting when possible.
• Leverage built-in mathematical functions.

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Summary

NumPy is the foundation of scientific computing in Python.

Key features include:

• Efficient arrays
• Fast computations
• Statistical functions
• Matrix operations
• Broadcasting
• Random number generation

Learning NumPy is essential for data science, machine learning, artificial intelligence, and numerical computing.

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Conclusion

NumPy is one of the most powerful and widely used Python libraries. Its ability to process large datasets efficiently makes it indispensable for modern software development, data analysis, and machine learning projects.

Mastering NumPy will significantly improve your Python programming skills and prepare you for advanced topics such as Pandas, Machine Learning, and Deep Learning.