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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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4 Hours13 Videos49 Exercises20,086 Learners4250 XPData Scientist with Python Track

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Course Description

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.
  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.

    Play Chapter Now
    Indexing and slicing arrays
    50 xp
    Slicing and indexing trees
    100 xp
    Stepping into 2D
    100 xp
    Sorting trees
    100 xp
    Filtering arrays
    50 xp
    Filtering with masks
    100 xp
    Fancy indexing vs. np.where()
    100 xp
    Creating arrays from conditions
    100 xp
    Adding and removing data
    50 xp
    Compatible or not?
    100 xp
    Adding rows
    100 xp
    Adding columns
    100 xp
    Deleting with np.delete()
    100 xp
  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.

    Play Chapter Now
    Summarizing data
    50 xp
    Sales totals
    100 xp
    Plotting averages
    100 xp
    Cumulative sales
    100 xp
    Vectorized operations
    50 xp
    Tax calculations
    100 xp
    Projecting sales
    100 xp
    Vectorizing .upper()
    100 xp
    Broadcasting
    50 xp
    Broadcastable or not?
    100 xp
    Broadcasting across columns
    100 xp
    Broadcasting across rows
    100 xp
  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.

    Play Chapter Now
    Saving and loading arrays
    50 xp
    Loading .npy files
    100 xp
    Getting help
    100 xp
    Update and save
    100 xp
    Array acrobatics
    50 xp
    Augmenting Monet
    100 xp
    Transposing your masterpiece
    100 xp
    Stacking and splitting
    50 xp
    2D split and stack
    100 xp
    Splitting RGB data
    100 xp
    Stacking RGB data
    100 xp
    Congratulations!
    50 xp

In the following tracks

Data Scientist with Python

Datasets

Monet RGB ArrayTree Census ArrayMonthly Sales ArraySudoku Game ArraySudoku Solution Array

Collaborators

James Chapman
Amy Peterson

Prerequisites

Intermediate Python
Izzy Weber Headshot

Izzy Weber

Curriculum Manager, DataCamp

Izzy is a Curriculum Manager at DataCamp. 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 for students.
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