Rafał Alitojć has completed

# Introduction to NumPy

4 hours
4,250 XP

## 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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1. 1

### Understanding NumPy Arrays

Free

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.

Play Chapter Now
Introducing arrays
50 xp
Your first NumPy array
100 xp
Creating arrays from scratch
100 xp
A range array
100 xp
Array dimensionality
50 xp
3D array creation
100 xp
The fourth dimension
100 xp
Flattening and reshaping
100 xp
NumPy data types
50 xp
The dtype argument
100 xp
Anticipating data types
100 xp
A smaller sudoku game
100 xp
2. 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. 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. 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.

In the following tracks

Data Manipulation

Collaborators

Prerequisites

Intermediate Python
Izzy Weber

Data Coach at iO-Sphere

Izzy is a Data Coach at iO-Sphere. She discovered a love for data during her seven years as an accounting professor at the University of Washington. She holds a masters degree in Taxation and is a Certified Public Accountant. Her passion is making learning technical topics fun.
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