Interactive Session 2B

🐍 Essentials of dictionaries

A cartoon drawing of the world’s largest dictionary. MidJourney 5

Getting Started

Before we begin our interactive session, please follow these steps to set up your Jupyter Notebook:

  1. Open JupyterLab and create a new notebook:
    • Click on the + button in the top left corner
    • Select Python 3.10.0 from the Notebook options
  2. Rename your notebook:
    • Right-click on the Untitled.ipynb tab
    • Select “Rename”
    • Name your notebook with the format: Session_2B_Dictionaries.ipynb
  3. Add a title cell:
    • In the first cell of your notebook, change the cell type to “Markdown”
    • Add the following content (replace the placeholders with the actual information):
# Day 2: Session B - Dictionaries

[Link to session webpage](https://eds-217-essential-python.github.io/course-materials/interactive-sessions/2b_dictionaries.html)

Date: 09/04/2024
  1. Add a code cell:
    • Below the title cell, add a new cell
    • Ensure it’s set as a “Code” cell
    • This will be where you start writing your Python code for the session
  2. Throughout the session:
    • Take notes in Markdown cells
    • Copy or write code in Code cells
    • Run cells to test your code
    • Ask questions if you need clarification
Caution

Remember to save your work frequently by clicking the save icon or using the keyboard shortcut (Ctrl+S or Cmd+S).

Let’s begin our interactive session!

Part 1: Basic Concepts with Species Lookup Table

Introduction to Dictionaries

Dictionaries in Python are collections of key-value pairs that allow for efficient data storage and retrieval. Each key maps to a specific value, making dictionaries ideal for representing real-world data in a structured format.

Probably the easiest mental model for thinking about structured data is a spreadsheet. You are all familiar with Excel spreadsheets, with their numbered rows and lettered columns. In the spreadsheet, data is often “structured” so that each row is an entry, and each column is perhaps a variable recorded for that entry.

structured-data

Instructions

We will work through this material together, writing a new notebook as we go.

Note

🐍     This symbol designates an important note about Python structure, syntax, or another quirk.

✏️   This symbol designates code you should add to your notebook and run.

Dictionaries

TLDR: Dictionaries are a very common collection type that allows data to be organized using a key:value framework. Because of the similarity between key:value pairs and many data structures (e.g. “lookup tables”), you will see Dictionaries quite a bit when working in python

The first collection we will look at today is the dictionary, or dict. This is one of the most powerful data structures in python. It is a mutable, unordered collection, which means that it can be altered, but elements within the structure cannot be referenced by their position and they cannot be sorted.

You can create a dictionary using the {}, providing both a key and a value, which are separated by a :.

Creating & Manipulating Dictionaries

We’ll start by creating a dictionary to store the common name of various species found in California’s coastal tidepools.

✏️ Try it. Add the cell below to your notebook and run it.

Code 
# Define a dictionary with species data containing latin names and corresponding common names.
species_dict = {
    "P ochraceus": "Ochre sea star",
    "M californianus": "California mussel",
    "H rufescens": "Red abalone"
}
Note 
🐍 <b>Note.</b> The use of whitespace and indentation is important in python. In the example above, the dictionary entries are indented relative to the brackets <code>{</code> and <code>}</code>. In addition, there is no space between the <code>'key'</code>, the <code>:</code>, and the <code>'value'</code> for each entry. Finally, notice that there is a <code>,</code> following each dictionary entry. This pattern is the same as all of the other <i>collection</i> data types we've seen so far, including <b>list</b>, <b>set</b>, and <b>tuple</b>.

Accessing elements in a dictionary

Accessing an element in a dictionary is easy if you know what you are looking for.

✏️ Try it. Add the cell below to your notebook and run it.

Code 
species_dict['M californianus']
'California mussel'

Adding a New Species

Because dictionaries are mutable, it is easy to add additional entries and doing so is straightforward. You specify the key and the value it maps to.

✏️ Try it. Add the cell below to your notebook and run it.

Code 
# Adding a new entry for Leather star
species_dict["D imbricata"] = "Leather star"

Accessing and Modifying Data

Accessing data in a dictionary can be done directly by the key, and modifications are just as direct.

✏️ Try it. Add the cell below to your notebook and run it.

Code 
# Accessing a species by its latin name
print("Common name for P ochraceus:", species_dict["P ochraceus"])
Common name for P ochraceus: Ochre sea star

✏️ Try it. Add the cell below to your notebook and run it.

Code 
# Updating the common name for Ochre Sea Star abalone
species_dict["P ochraceus"] = "Purple Starfish"
print("Updated data for Pisaster ochraceus:", species_dict["P ochraceus"])
Updated data for Pisaster ochraceus: Purple Starfish

Removing a Dictionary Element

✏️ Try it. Add the cell below to your notebook and run it.

Code 
# Removing "P ochraceus"
del species_dict["P ochraceus"]
print(f"Deleted data for Pisaster ochraceus, new dictionary: {species_dict}")
Deleted data for Pisaster ochraceus, new dictionary: {'M californianus': 'California mussel', 'H rufescens': 'Red abalone', 'D imbricata': 'Leather star'}

Accessing dictionary keys and values

Every dictionary has builtin methods to retrieve its keys and values. These functions are called, appropriately, keys() and values()

✏️ Try it. Add the cell below to your notebook and run it.

Code 
# Accessing the dictionary keys:
latin_names = species_dict.keys()
print(f"Dictionary keys (latin names): {latin_names}")
Dictionary keys (latin names): dict_keys(['M californianus', 'H rufescens', 'D imbricata'])

✏️ Try it. Add the cell below to your notebook and run it.

Code 
# Accessing the dictionary values
common_names = species_dict.values()
print(f"Dictionary values (common names): {common_names}")
Dictionary values (common names): dict_values(['California mussel', 'Red abalone', 'Leather star'])
Note

🐍 Note. The keys() and values() functions return a dict_key object and dict_values object, respectively. Each of these objects contains a list of either the keys or values. You can force the result of the keys() or values() function into a list by wrapping either one in a list() command.

Looping through Dictionaries

Python has an efficient way to loop through all the keys and values of a dictionary at the same time. The items() method returns a tuple containing a (key, value) for each element in a dictionary. In practice this means that we can loop through a dictionary in the following way:

Code 
my_dict = {'name': 'Homer Simpson',
           'occupation': 'nuclear engineer',
           'address': '742 Evergreen Terrace',
           'city': 'Springfield',
           'state': ' ? '
          }

for key, value in my_dict.items():
    print(f"{key.capitalize()}: {value}.")
Name: Homer Simpson.
Occupation: nuclear engineer.
Address: 742 Evergreen Terrace.
City: Springfield.
State:  ? .

✏️ Try it. Add the cell below to your notebook and run it.

Add a new code cell and code to loop through the species_dict dictionary and print out a sentence providing the common name of each species (e.g. “The common name of M californianus” is…“).

Accessing un-assigned elements in Dictionaries

Attempting to retrieve an element of a dictionary that doesn’t exist is the same as requesting an index of a list that doesn’t exist - Python will raise an Exception. For example, if you attempt to retrieve the definition of a field that hasn’t been defined, then you get an error:

✏️ Try it. Add the cell below to your notebook and run it.

species_dict["E dofleini"]

You should get a KeyError exception:

KeyError: ‘E dofleini’

To avoid getting an error when requesting an element from a dict, you can use the get() function. The get() function will return None if the element doesn’t exist:

✏️ Try it. Add the cell below to your notebook and run it.

Code 
species_description = species_dict.get("E dofleini")
print("Accessing non-existent latin name, E dofleini:\n", species_description)
Accessing non-existent latin name, E dofleini:
 None

You can also provide an argument to python to return if the item isn’t found:

✏️ Try it. Add the cell below to your notebook and run it.

Code 
species_description = species_dict.get("E dofleini", "Species not found in dictionary")
print("Accessing non-existent latin name, E dofleini:\n", species_description)
Accessing non-existent latin name, E dofleini:
 Species not found in dictionary

Summary and Additional Resources

We’ve explored the creation, modification, and application of dictionaries in Python, highlighting their utility in storing structured data. As you progress in Python, you’ll find dictionaries indispensable across various applications, from data analysis to machine learning.

For further study, consult the following resources: - Python’s Official Documentation on Dictionaries - Our class Dictionary Cheatsheet

End interactive session 2B