Dictionaries

Overview

Teaching: 20 min
Exercises: 15 min

Questions

How can I store key-value data?

Objectives

Store data in a Python dictionary

Extract data from a Python dictionary

Python provides a data type called a dictionary, which is similar to a list in that it is a collection of objects.

Dictionaries and lists share the following characteristics:

Both are mutable.
Both are dynamic. They can grow and shrink as needed.
Both can be nested. A list can contain another list. A dictionary can contain another dictionary. A dictionary can also contain a list, and vice versa.

Dictionaries differ from lists primarily in how elements are accessed:

List elements are accessed by their position in the list, via indexing.
Dictionary elements are accessed via keys.

This section aims to provide a good sense of when a dictionary is the appropriate data type to use, and how to do so.

Creating a dictionary

We can define a dictionary by enclosing a comma-separated list of key-value pairs in curly braces ({}). A colon (:) separates each key from its associated value:

person = {
    'name': 'Ahmed',
    'age': 42
}

Once we have defined a dictionary, we can inspect its type and values:

print(type(person))
print(person)

<class 'dict'>
{'name': 'Ahmed', 'age': 42}

A value is retrieved from a dictionary by specifying its corresponding key in square brackets ([]):

print(person['name'])
print(person['age'])

Ahmed
42

Updating a dictionary

Let’s create a dictionary, containing the details for multiple individuals, where the details for each individual are also stored as dictionaries:

people = {
    'Ahmed': {
        'age': 42
    },
    'Cheryl': {
        'age': 33
    }
}

print(people)

{'Ahmed': {'age': 42}, 'Cheryl': {'age': 33}}

We can add a new entry to a dictionary by defining a new key:

people['Susan'] = {'age': 25}

print(people)

{'Ahmed': {'age': 42}, 'Cheryl': {'age': 33}, 'Susan': {'age': 25}}

An existing value in a dictionary can be updated by redefining the value associated with an existing key:

people['Cheryl']['age'] = 34

print(people)

{'Ahmed': {'age': 42}, 'Cheryl': {'age': 34}, 'Susan': {'age': 25}}

The available keys in a dictionary can be accessed using the dictionary’s .keys() method:

print(people.keys())

dict_keys(['Ahmed', 'Cheryl', 'Susan'])

Using a dictionary to store data

In our data/ directory, we have three files containing temperature data for June, July and August 2022:

data/temperature_2022-06.csv
data/temperature_2022-07.csv
data/temperature_2022-08.csv

We can create a dictionary to store this information:

temp_files = {
    'June': 'data/temperature_2022-06.csv',
    'July': 'data/temperature_2022-07.csv',
    'August': 'data/temperature_2022-08.csv'
}

print(temp_files)

{'June': 'data/temperature_2022-06.csv', 'July': 'data/temperature_2022-07.csv', 'August': 'data/temperature_2022-08.csv'}

If we would like to create a single figure containing three plots, one for each month, we can do this by accessing values from the dictionary:

# Create the subplot axes, with a single row and three columns:
fig, axs = plt.subplots(nrows=1, ncols=3)

# Set the figure size:
fig.set_figwidth(24)
fig.set_figheight(8)

# Plot June data
# access the axes for this plot:
ax = axs[0]
# Set the month name:
month = 'June'
# Read the data for this month using Pandas:
data = pd.read_csv(temp_files[month], index_col='Date', parse_dates=['Date'])
# Plot the data using the Pandas DataFrame plot function:
data.plot(ax=ax)
# Set the plot title:
ax.set_title(month)

# Plot July data
# access the axes for this plot:
ax = axs[1]
# Set the month name:
month = 'July'
# Read the data for this month using Pandas:
data = pd.read_csv(temp_files[month], index_col='Date', parse_dates=['Date'])
# Plot the data using the Pandas DataFrame plot function:
data.plot(ax=ax)
# Set the plot title:
ax.set_title(month)

# Plot August data
# access the axes for this plot:
ax = axs[2]
# Set the month name:
month = 'August'
# Read the data for this month using Pandas:
data = pd.read_csv(temp_files[month], index_col='Date', parse_dates=['Date'])
# Plot the data using the Pandas DataFrame plot function:
data.plot(ax=ax)
# Set the plot title:
ax.set_title(month)

Temperature Plot 1

The three plots in the figure do not have the same y axis limits, which makes it difficult to compare the values.

We can fix this by setting the y axis limits, using the set_ylim function:

# Create the subplot axes, with a single row and three columns:
fig, axs = plt.subplots(nrows=1, ncols=3)

# Set the figure size:
fig.set_figwidth(24)
fig.set_figheight(8)

# Plot June data
# access the axes for this plot:
ax = axs[0]
# Set the month name:
month = 'June'
# Read the data for this month using Pandas:
data = pd.read_csv(temp_files[month], index_col='Date', parse_dates=['Date'])
# Plot the data using the Pandas DataFrame plot function:
data.plot(ax=ax)
# Set the y axis limits:
ax.set_ylim((0, 40))
# Set the plot title:
ax.set_title(month)

# Plot July data
# access the axes for this plot:
ax = axs[1]
# Set the month name:
month = 'July'
# Read the data for this month using Pandas:
data = pd.read_csv(temp_files[month], index_col='Date', parse_dates=['Date'])
# Plot the data using the Pandas DataFrame plot function:
data.plot(ax=ax)
# Set the y axis limits:
ax.set_ylim((0, 40))
# Set the plot title:
ax.set_title(month)

# Plot August data
# access the axes for this plot:
ax = axs[2]
# Set the month name:
month = 'August'
# Read the data for this month using Pandas:
data = pd.read_csv(temp_files[month], index_col='Date', parse_dates=['Date'])
# Plot the data using the Pandas DataFrame plot function:
data.plot(ax=ax)
# Set the y axis limits:
ax.set_ylim((0, 40))
# Set the plot title:
ax.set_title(month)

Temperature Plot 2

Plotting in a loop

Can you recreate the figure above by looping through the values in the dictionary. You may wish to use the enumerate function.

Solution

# Create the subplot axes, with a single row and three columns:
fig, axs = plt.subplots(nrows=1, ncols=3)

# Set the figure size:
fig.set_figwidth(24)
fig.set_figheight(8)

# Loop through dictionary keys, using the enumerate function:
for index, month in enumerate(temp_files.keys()):
    # Access the axes for this plot:
    ax = axs[index]
    # Set the month name from the dictoinary key name:
    month = month
    # Read the data for this month using Pandas:
    data = pd.read_csv(temp_files[month], index_col='Date', parse_dates=['Date'])
    # Plot the data using the Pandas DataFrame plot function:
    data.plot(ax=ax)
    # Set the y axis limits:
    ax.set_ylim((0, 40))
    # Set the plot title:
    ax.set_title(month)

Temperature Plot 2

Key Points

Python dictionaries are one of the most versatile and efficient data types in Python.

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