Lesson List

Welcome to AI Programming with Python

Start using AI techniques and developing skills related to programming, linear algebra, and neural networks.

Why Python Programming

Start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly.

Data Types and Operators

Control Flow

Functions

Scripting

Lab Classifying Images

In this project, learners will be testing their newly-acquired Python coding skills by using a trained image classifier. They will need to use the trained neural network to classify images of dogs (by breeds) and compare the output with the known dog breed classification. Learners will have a chance to build their own functions, use command line arguments, test the runtime of the code, create a dictionary of lists, and more.

NumPy

Learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, Pandas, and Matplotlib.

Pandas

Matplotlib and Seaborn Part 1

Learn how to use Matplotlib to choose appropriate plots for one and two variables based on the types of data you have.

Matplotlib and Seaborn Part 2

Introduction

Learn the foundational math needed for AI success—vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks.

Vectors

Linear Combination

Linear Transformation and Matrices

Vectors Lab

Linear Combination Lab

Linear Mapping Lab

Linear Algebra in Neural Networks

Introduction to Neural Networks

Gain a solid foundation in the latest trends in AI: neural networks, deep learning, and PyTorch.

Implementing Gradient Descent

Training Neural Networks

Deep Learning with PyTorch

Create Your Own Image Classifier

How Do I Continue From Here

Go to course home
31. Dictionaries and Identity Operators

Dictionaries and Identity Operators

Dictionaries

A dictionary is a mutable data type that stores mappings of unique keys to values. Here’s a dictionary that stores elements and their atomic numbers.

elements = {"hydrogen": 1, "helium": 2, "carbon": 6}

Dictionaries can have keys of any immutable type, like integers or tuples, not just strings. It’s not even necessary for every key to have the same type! We can look up values or insert new values in the dictionary using square brackets that enclose the key.

print(elements["helium"])  # print the value mapped to "helium"
elements["lithium"] = 3  # insert "lithium" with a value of 3 into the dictionary

We can check whether a value is in a dictionary the same way we check whether a value is in a list or set with the in keyword. Dicts have a related method that’s also useful, get. get looks up values in a dictionary, but unlike square brackets, get returns None (or a default value of your choice) if the key isn’t found.

print("carbon" in elements)
print(elements.get("dilithium"))

This would output:

True
None

Carbon is in the dictionary, so True is printed. Dilithium isn’t in our dictionary so None is returned by get and then printed. If you expect lookups to sometimes fail, get might be a better tool than normal square bracket lookups because errors can crash your program.

Identity Operators

Keyword Operator
is evaluates if both sides have the same identity
is not evaluates if both sides have different identities

You can check if a key returned None with the is operator. You can check for the opposite using is not.

n = elements.get("dilithium")
print(n is None)
print(n is not None)

This would output:

True
False
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