Learn Numpy - 10 Code Examples & CST Typing Practice Test

NumPy (Numerical Python) is an open-source Python library that provides high-performance, multi-dimensional arrays and a wide range of mathematical functions to operate on these arrays, forming the foundation of scientific computing in Python.

View all 10 Numpy code examples →

NumPy Array Operations Example NumPy Array Indexing and Slicing NumPy Random Number Generation NumPy Array Reshaping NumPy Mathematical Functions NumPy Boolean Indexing NumPy Stacking Arrays NumPy Linear Algebra Operations NumPy Statistical Analysis NumPy Broadcasting Example

Learn NUMPY with Real Code Examples

Updated Nov 24, 2025

Introduction & Fundamentals Setup & Configuration Architecture & Deep Internals Performance & Security Development Workflow Learning & Career Growth Business & Strategy Examples

Code Sample Descriptions

NumPy Array Operations Example

import numpy as np

# Create arrays
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([5, 6, 7, 8])

# Array arithmetic
sum_arr = arr1 + arr2
print('Sum:', sum_arr)

# Statistical operations
print('Mean of arr1:', np.mean(arr1))
print('Standard deviation of arr2:', np.std(arr2))

# Multi-dimensional arrays
matrix = np.array([[1,2],[3,4]])
print('Matrix:
', matrix)
print('Transpose:
', matrix.T)

A minimal NumPy example demonstrating array creation, arithmetic, and basic statistics.

Let’s Try →

NumPy Array Indexing and Slicing

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
print('Original array:', arr)

# Indexing
print('Element at index 2:', arr[2])

# Slicing
print('Elements from index 1 to 3:', arr[1:4])
print('Every second element:', arr[::2])

Demonstrates indexing and slicing of NumPy arrays.

Let’s Try →

NumPy Random Number Generation

import numpy as np

# Random integers
rand_ints = np.random.randint(0, 10, size=5)
print('Random integers:', rand_ints)

# Random floats
rand_floats = np.random.rand(3,3)
print('Random floats 3x3:\n', rand_floats)

Generating random numbers and arrays using NumPy.

Let’s Try →

NumPy Array Reshaping

import numpy as np

arr = np.arange(12)
print('Original array:', arr)

matrix = arr.reshape((3,4))
print('Reshaped 3x4 matrix:\n', matrix)

flattened = matrix.flatten()
print('Flattened array:', flattened)

Changing the shape of arrays.

Let’s Try →

NumPy Mathematical Functions

import numpy as np

arr = np.array([1,4,9,16])
print('Square root:', np.sqrt(arr))
print('Exponential:', np.exp(arr))
print('Natural log:', np.log(arr))

Applying mathematical functions on arrays.

Let’s Try →

NumPy Boolean Indexing

import numpy as np

arr = np.array([10, 15, 20, 25, 30])
print('Original array:', arr)

mask = arr > 20
print('Boolean mask:', mask)
print('Filtered array:', arr[mask])

Filtering arrays using boolean conditions.

Let’s Try →

NumPy Stacking Arrays

import numpy as np

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

vstacked = np.vstack((arr1, arr2))
hstacked = np.hstack((arr1, arr2))
print('Vertical stack:\n', vstacked)
print('Horizontal stack:', hstacked)

Combining arrays using vertical and horizontal stacking.

Let’s Try →

NumPy Linear Algebra Operations

import numpy as np

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

# Matrix multiplication
C = np.dot(A, B)
print('Matrix multiplication:\n', C)

# Determinant
print('Determinant of A:', np.linalg.det(A))

# Inverse
print('Inverse of A:\n', np.linalg.inv(A))

Matrix multiplication, determinant, and inverse using NumPy.

Let’s Try →

NumPy Statistical Analysis

import numpy as np

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

print('Mean:', np.mean(arr))
print('Median:', np.median(arr))
print('Std deviation:', np.std(arr))
print('Correlation:', np.corrcoef(arr, arr2))

Compute mean, median, standard deviation, and correlation.

Let’s Try →

NumPy Broadcasting Example

import numpy as np

arr = np.array([[1,2,3],[4,5,6]])
scalar = 10

# Add scalar to array
print('Add scalar:\n', arr + scalar)

# Subtract row vector
row_vec = np.array([1,0,1])
print('Subtract row vector:\n', arr - row_vec)

Demonstrates broadcasting operations between arrays of different shapes.

Let’s Try →

Frequently Asked Questions about Numpy

What is Numpy?

What are the primary use cases for Numpy?

Numerical computations with large datasets. Matrix operations, linear algebra, and array manipulations. Data preprocessing for machine learning and AI. Scientific simulations and mathematical modeling. Integration with other Python libraries for analytics and visualization

What are the strengths of Numpy?

Highly optimized and fast for numerical computations. Foundation for most Python scientific libraries. Extensive community support and documentation. Supports large datasets efficiently. Flexible array operations with broadcasting and vectorization

What are the limitations of Numpy?

Not a machine learning library by itself. Limited built-in plotting and visualization. Pure Python loops over arrays are slow; vectorization is required. Single-core by default (needs libraries like NumExpr for multi-core). No native support for GPU acceleration

How can I practice Numpy typing speed?

CodeSpeedTest offers 10+ real Numpy code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.

Learn Other Programming Languages

C React Python C++Rust TypeScript Kotlin PHP Java C#Ruby Mql Cql N1ql Cypher Gremlin Partiql Haskell Elixir Fsharp View all languages →

Code Sample Descriptions

NumPy Array Operations Example

import numpy as np

# Create arrays
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([5, 6, 7, 8])

# Array arithmetic
sum_arr = arr1 + arr2
print('Sum:', sum_arr)

# Statistical operations
print('Mean of arr1:', np.mean(arr1))
print('Standard deviation of arr2:', np.std(arr2))

# Multi-dimensional arrays
matrix = np.array([[1,2],[3,4]])
print('Matrix:
', matrix)
print('Transpose:
', matrix.T)

A minimal NumPy example demonstrating array creation, arithmetic, and basic statistics.

Let’s Try →

NumPy Array Indexing and Slicing

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
print('Original array:', arr)

# Indexing
print('Element at index 2:', arr[2])

# Slicing
print('Elements from index 1 to 3:', arr[1:4])
print('Every second element:', arr[::2])

Demonstrates indexing and slicing of NumPy arrays.

Let’s Try →

NumPy Random Number Generation

import numpy as np

# Random integers
rand_ints = np.random.randint(0, 10, size=5)
print('Random integers:', rand_ints)

# Random floats
rand_floats = np.random.rand(3,3)
print('Random floats 3x3:\n', rand_floats)

Generating random numbers and arrays using NumPy.

Let’s Try →

NumPy Array Reshaping

import numpy as np

arr = np.arange(12)
print('Original array:', arr)

matrix = arr.reshape((3,4))
print('Reshaped 3x4 matrix:\n', matrix)

flattened = matrix.flatten()
print('Flattened array:', flattened)

Changing the shape of arrays.

Let’s Try →

NumPy Mathematical Functions

import numpy as np

arr = np.array([1,4,9,16])
print('Square root:', np.sqrt(arr))
print('Exponential:', np.exp(arr))
print('Natural log:', np.log(arr))

Applying mathematical functions on arrays.

Let’s Try →

NumPy Boolean Indexing

import numpy as np

arr = np.array([10, 15, 20, 25, 30])
print('Original array:', arr)

mask = arr > 20
print('Boolean mask:', mask)
print('Filtered array:', arr[mask])

Filtering arrays using boolean conditions.

Let’s Try →

NumPy Stacking Arrays

import numpy as np

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

vstacked = np.vstack((arr1, arr2))
hstacked = np.hstack((arr1, arr2))
print('Vertical stack:\n', vstacked)
print('Horizontal stack:', hstacked)

Combining arrays using vertical and horizontal stacking.

Let’s Try →

NumPy Linear Algebra Operations

import numpy as np

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

# Matrix multiplication
C = np.dot(A, B)
print('Matrix multiplication:\n', C)

# Determinant
print('Determinant of A:', np.linalg.det(A))

# Inverse
print('Inverse of A:\n', np.linalg.inv(A))

Matrix multiplication, determinant, and inverse using NumPy.

Let’s Try →

NumPy Statistical Analysis

import numpy as np

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

print('Mean:', np.mean(arr))
print('Median:', np.median(arr))
print('Std deviation:', np.std(arr))
print('Correlation:', np.corrcoef(arr, arr2))

Compute mean, median, standard deviation, and correlation.

Let’s Try →

NumPy Broadcasting Example

import numpy as np

arr = np.array([[1,2,3],[4,5,6]])
scalar = 10

# Add scalar to array
print('Add scalar:\n', arr + scalar)

# Subtract row vector
row_vec = np.array([1,0,1])
print('Subtract row vector:\n', arr - row_vec)

Demonstrates broadcasting operations between arrays of different shapes.

Let’s Try →

Frequently Asked Questions about Numpy

What is Numpy?

What are the primary use cases for Numpy?

What are the strengths of Numpy?

What are the limitations of Numpy?

How can I practice Numpy typing speed?

CodeSpeedTest offers 10+ real Numpy code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.