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
Trueif all elements in an array evaluate toTrue. - 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.