Python NumPy

This Python NumPy Tutorial helps you learn NumPy from scratch so that you can use it effectively in your data science & machine learning projects.

What you’ll learn

• Create single and multi-dimensional NumPy arrays
• Effectively use NumPy built-in functions & methods
• Perform mathematical operations on arrays
• Extract elements from arrays using slicing and indexing
• Select elements of arrays conditionally.

Section 1. Getting started

• What is NumPy – learn what NumPy is and what it can do for you.

Section 2. Creating arrays

• Creating arrays – show you how to create numpy arrays.
• zeros() – create a numpy array of a given shape whose elements are filled with zeros.
• ones() – create a numpy array of a given shape whose elements are filled with ones.
• arange() –  create a numpy array with evenly spaced numbers.
• linspace() – create a new numpy array with evenly spaced numbers of a specified interval.

Section 3. Array indexing & slicing

• Indexing – learn how to select elements using indexing.
• Slicing – show you how to use slices to extract elements from an array.
• Fancy indexing – learn how to index a numpy array using another numpy array.
• Boolean indexing – guide you on how to index an array using another array of boolean values.
• View vs. copy – show you the difference between a view & copy of an array and how to use the copy() method to make a copy of an array.

Section 4. Aggregate functions

• sum()– return the sum of all elements
• mean() – return the average of all elements in an array.
• var() – return the variance of all elements in an array.
• std() – calculate the standard deviation of elements of an array.
• prod() – return the product of all elements.
• amin() – return the minimum value in an array.
• amax() – return the maximum value in an array.
• all() – return `True` if all elements in an array evaluate to `True`.
• any() – return True if any of the elements in an array is nonzero.

Section 5. Array operations

• reshape() – give an array a new shape while keeping the same elements.
• transpose() – return a view of an array with axes transposed.
• sort() – return a sorted copy of an array.
• flatten() – return a copy of an array collapsed into one dimension.
• ravel() – return a contiguous flattened array.

Section 6. Arithmetic operations

• add() – return the sum of two equal-sized arrays.
• subtract() – return the difference between two equal-sized arrays.
• multiply() – return the product of two equal-sized arrays.
• divide() – return the quotient of two equal-sized arrays.
• Broadcasting – show you how NumPy uses broadcasting to perform arithmetic operations on arrays with different shapes.

Section 7. Joining & splitting arrays

• concatenate() – join two or more arrays along an existing axis.
• stack() – join two or more arrays along a new axis.
• vstack() – join two or more arrays vertically.
• hstack() – join two or more arrays horizontally.
• split() – split an array into subarrays.
Did you find this tutorial helpful ?