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.

Introducing arrays

Your first NumPy array

Creating arrays from scratch

A range array

Array dimensionality

3D array creation

The fourth dimension

Flattening and reshaping

NumPy data types

The dtype argument

Anticipating data types

A smaller sudoku game

Understanding NumPy Arrays

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.

Indexing and slicing arrays

Slicing and indexing trees

Stepping into 2D

Sorting trees

Filtering arrays

Filtering with masks

Fancy indexing vs. np.where()

Creating arrays from conditions

Adding and removing data

Options

Compatible along the first axis

Compatible along the second axis

Not compatible

Compatible or not?

Adding rows

Adding columns

Deleting with np.delete()

Selecting and Updating Data

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.

Summarizing data

Sales totals

Plotting averages

Cumulative sales

Vectorized operations

Tax calculations

Projecting sales

Vectorizing .upper()

Broadcasting

Broadcastable or not?

Broadcasting across columns

Broadcasting across rows

Array Mathematics!

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.

Saving and loading arrays

Loading .npy files

Getting help

Update and save

Array acrobatics

Augmenting Monet

Transposing your masterpiece

Stacking and splitting

2D split and stack

Splitting RGB data

Stacking RGB data

Congratulations!

Array Transformations

Monet RGB Array

Tree Census Array

Monthly Sales Array

Sudoku Game Array

Sudoku Solution Array

Course Glossary

NumPy is an essential Python library. TensorFlow and scikit-learn use NumPy arrays as inputs, and pandas and Matplotlib are built on top of NumPy. In this Introduction to NumPy course, you'll become a master wrangler of NumPy's core object: arrays! Using data from New York City's tree census, 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. By the end of the course, you'll be using 3D arrays to alter a Claude Monet painting, and you'll understand why such array alterations are essential tools for machine learning.

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.

The course glossary can be found on the right in the resources section.

To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

<h2>Explore Python's Data Science package: NumPy</h2>
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. 
<br><br>
<h2>Discover NumPy Arrays</h2>
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.
<br><br>
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. 
<br><br>
<h2>Gain Confidence by Practicing on the Monet dataset</h2>
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.
<br><br>
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.

Intermediate Python

Get to know Python’s popular data science package; NumPy. In four hours, you'll create, sort, filter, and update arrays using New York City’s tree census.

Introduction to NumPy

Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.

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Fancy indexing vs. np.where()

Introduction to NumPy

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