Course
Introduction to NumPy
BasicSkill Level
Updated 12/2025Start Course for Free
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PythonData Manipulation4 hr13 videos49 Exercises4,250 XP58,082Statement of Accomplishment
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Explore Python's Data Science package: NumPy
Gain an introduction to Numpy and understand why this Python library is essential to all Python data scientists and analysts. Most importantly, learn more about Numpy arrays and how to create and change array shapes to suit your needs.Discover NumPy Arrays
NumPy is an essential Python library for data scientists and analysts. It offers a great alternative to Python lists, as they are more compact and allow faster access to reading and writing items, making them a more convenient and efficient option.In this Introduction to NumPy course, you'll become a master wrangler of NumPy's core object: arrays! Using New York City's tree census data, you'll create, sort, filter, and update arrays. You'll discover why NumPy is so efficient and use broadcasting and vectorization to make your NumPy code even faster.
Gain Confidence by Practicing on the Monet dataset
By the last chapter, you will use your newly acquired knowledge to perform array transformations. You will use image 3D arrays to alter a Claude Monet painting and understand why such array alterations are essential tools for machine learning.You will gain confidence in Numpy arrays and their different operations upon course completion. This course is part of the Data Scientist with Python track and is perfect for those seeking a Data Science certification with DataCamp.
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Start Course for FreeWhat you'll learn
- Assess array transformation workflows that load, save, reshape, split, stack, transpose, flip, and modify RGB image data for analytical or machine-learning tasks
- Differentiate array creation techniques—including list conversion, np.zeros, np.random.random, and np.arange—when building arrays of specified shapes and data types
- Evaluate vectorized arithmetic, aggregation, and broadcasting operations to determine their effects on arrays with compatible or incompatible shapes
- Identify the characteristics and memory advantages of NumPy n-dimensional arrays compared to Python lists
- Recognize correct methods for indexing, slicing, masking, fancy indexing, concatenating, and deleting data to manipulate elements along defined axes
Prerequisites
Intermediate Python1
Understanding NumPy Arrays
Meet the incredible NumPy array! Learn how to create and change array shapes to suit your needs. Finally, discover NumPy's many data types and how they contribute to speedy array operations.
2
Selecting and Updating Data
Sharpen your NumPy data wrangling skills by slicing, filtering, and sorting New York City’s tree census data. Create new arrays by pulling data based on conditional statements, and add and remove data along any dimension to suit your purpose. Along the way, you’ll learn the shape and dimension compatibility principles to prepare for super-fast array math.
3
Array Mathematics!
Leverage NumPy’s speedy vectorized operations to gather summary insights on sales data for American liquor stores, restaurants, and department stores. Vectorize Python functions for use in your NumPy code. Finally, use broadcasting logic to perform mathematical operations between arrays of different sizes.
4
Array Transformations
NumPy meets the art world in this final chapter as we use image data from a Monet masterpiece to explore how you can use to augment image data. You’ll use flipping and transposing functionality to quickly transform our masterpiece. Next, you’ll pull the Monet array apart, make changes, and reconstruct it using array stacking to see the results.
Introduction to NumPy
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Enroll NowFAQs
What is a NumPy array?
A NumPy array is a multi-dimensional object in Python where you can perform calculations over entire arrays. You can access it from the NumPy package.
What is NumPy used for?
NumPy, together with pandas, is one of the most popular and used Python packages in the world. It is mainly used to work with arrays and their functions. These functions include performing linear algebra and matrices. Thus, many data professionals, from machine learning engineers to financial analysts, depend on this package to perform extensive data science tasks.
Why is NumPy used in Machine Learning?
Unlike Pandas, toolkits for machine or deep learning such as Tensorflow or scikit can only use NumPy arrays, meaning that machine learning engineers often prefer NumPy for their tasks. Additionally, with its built-in functions, machine learning engineers can perform lighting speed calculations needed for their tasks.
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