NumPy Learning Path

Get started with the fundamental package for scientific computing with Python

Getting Started with NumPy

NumPy is a foundational Python library for data science, math, and scientific computing. If you’re not coming from a scientific or math background, NumPy can seem intimidating. But, don’t worry! The tutorials below get you started with the main concepts of NumPy and really demonstrate its power.

NumPy for Data Science

Get introduced to NumPy, including learning how it’s used in data science.

Indexing and Slicing NumPy Arrays

Learn how to access items in NumPy arrays, using special methods reserved for NumPy.

Using the NumPy where() Function

Learn how to process NumPy arrays conditionally.

Creating NumPy Arrays

NumPy arrays are a foundational component of NumPy. Learning how to create them programmatically is a critical skill in working with NumPy. The library provides a ton of flexibility in creating useful arrays, such as sequences of numbers or even evenly-spaced arrays.

NumPy arange() Function

Learn how to create sequences of numbers using the NumPy arange() function.

NumPy linspace() Function

Learn how to create evenly spaced arrays using the NumPy linspace() function.

NumPy logspace() Function

Learn how to create evenly spaced arrays on a log scale using the NumPy logspace() function.

NumPy zeros() Function

Learn how to create arrays of zeros using the NumPy zeros() function.

NumPy full() Function

Learn how to create arrays of a single value using the NumPy full() function.

NumPy Normal Distribution

Learn how to create random normal distributions using the NumPy normal() function.

Modifying Arrays in NumPy

Once you’ve created your arrays in NumPy, you may want to modify them. NumPy makes modifying array items easier, allowing you to map functions to each item in an array. Learn how to normalize and limit array values, map a function over each item, and so much more.

How to Concatenate NumPy Arrays

How to join and concatenate multiple NumPy arrays into a single array.

How to Map a Function Over a NumPy Array

How to apply a function to every element in a NumPy array.

How to Normalize NumPy Arrays

How to normalize the values in a NumPy array.

How to Limit Array Values with np.clip()

How to clip minimum and maximum values in a NumPy array to prevent extreme values.

How to Pad NumPy Arrays with np.pad()

How to pad a NumPy array with values to modify their size.

How to Stack NumPy Arrays with np.stack()

How to stack NumPy arrays horizontally and vertically.

Reshaping Arrays in NumPy

Being able to reshape your arrays is an essential skill once you move into machine learning and deep learning. NumPy makes reshaping arrays incredibly simple and intuitive. The resources below guide you through the key functions you need to know.

How to Reduce Dimensionality of NumPy Arrays

Learn how to use the np.squeeze() function to reduce the dimensions of an array.

How to Tile NumPy Arrays with np.tile()

Learn how to repeat NumPy arrays in a pattern using np.tile()

How to Flatten a NumPy Array

Learn how to flatten a NumPy array using the np.flatten() function

Calculating Values with NumPy

NumPy wouldn’t be a math library without being able to calculate values. The resources below provide essential guides to working with NumPy arrays, including finding values and calculating histograms.