Basics of Python and Introduction to Quarto in Visual Studio Code

Mustafa Aslan, PhD Researcher
Data Lab for Social Good Research Group
Cardiff University, UK

3 Mar 2025

Outline

• Introduction to VS

• Installing and Importing Libraries in Python

• Getting Help in Python

• Introduction to Quarto

Introduction to VS Code

Introduction to VS Code

What is VS Code

• A free, and open-source code editor developed by Microsoft.
• Supports multiple programming languages and frameworks.

Key Features

• Integrated Terminal: Access a terminal directly in VS Code for running commands.
• Extensions: Add functionality for specific languages, tools, or frameworks (e.g., Quarto, Python, R).
• Source Control: Built-in Git integration for version control.
• Debugging: Powerful debugging tools for various languages.
• Customizable Interface: Themes, layout, and key bindings to suit user preferences.

Installing and Importing Libraries

What is library?

• A collection of pre-written code that contains functions and modules that developers can use to solve programming tasks

• Python libraries cover libraries for a wide range of tasks, including:

  • pandas and numpy for data analysis, cleaning, exploration, and efficient numerical computations.
  • scikit-learn, TensorFlow for machine learning tasks such as prediction, clustering, and forecasting.
  • statsforecast, darts and skforecast for forecasting tasks

What is library?

What is PIP?

• PIP is a package manager for Python packages or modules
• PIP is used to install and manage Python libraries

Installing a Library

• 1. Open a terminal or command prompt.
• 2. Use the command pip install <library-name>.
  • Example: pip install numpy
• 3. Alternatively, run the following code in a notebook to install libraries directly: python !pip install numpy

Importing libraries or modules

• Once a library is installed, you need to import it into your Python script:

  • Example:

    import numpy

• Use as to create an alias for convenience (e.g., import pandas as pd or import numpy as np).

• Use from … import … to call specific functions from libraries or modules :

  • Example:

    from math import sqrt
    print(sqrt(16))  # Output: 4.0

Namespaces

• A namespace is a system that has a unique name for each and every object in Python.
• An object might be a variable or a method.
• Some functions like print(), id() are always present, these are built-in namespaces.
• When a user creates a module, a global namespace gets created, later the creation of local functions creates the local namespace.

Example:

x = "global"

def outer_function():
    # Enclosing namespace
    x = "enclosing"
    
    def inner_function():
        # Local namespace
        x = "local"
        print("Inner:", x)  # Prints: Inner: local

    inner_function()
    print("Outer:", x)  # Prints: Outer: enclosing

outer_function()
print("Global:", x)  # Prints: Global: global

Getting Help in Python

Using built-in help functions

• Python provides built-in tools to explore libraries and and functions
• Some of them are:
  • help(): Displays documentation for an object.
  • dir(): Lists the attributes and methods of an object
  • type(): Displays the type of the object

Using built-in help functions

Examples:

Example -1:

print(help(len))
# output:

Help on built-in function len in module builtins:

len(obj, /)
    Return the number of items in a container.

None

Example -2:

print(dir(list))
# output:

['__add__', '__class__', '__class_getitem__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']

Example -3:

x = "cardiff"
y = 5
z = [1,2,3,4,5]

print(type(x))
print(type(y))
print(type(z))
# output:

<class 'str'>
<class 'int'>
<class 'list'>

Introduction to Quarto

Three Main Tasks Using Quarto

Quarto help us publish reproducible, production quality articles, presentations, dashboards, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more.

• Writing: Combine Markdown, code, and visualizations in .qmd files.
• Render: Generate output formats like HTML, PDF, or slides.
• Publish: Share documents or host on platforms like GitHub.

How to Use Quarto in VS Code

1. Install the Quarto Extension

• Open VS Code.
• Go to the Extensions view by clicking on the Extensions icon in the Activity Bar on the side of the window or press Ctrl+Shift+X.
• Search for “Quarto”.
• Install the Quarto extension on you vs code

2. Create a Quarto Document

• Go to File > New File.
• Save the file with a .qmd extension (e.g., example.qmd).
• Start writing your Quarto content.

3. Render a Quarto Document

• To render and preview, execute the Quarto: “Preview” command.
• If you want to preview a different format, use the Quarto: Preview Format command and select the format you want to render to (e.g., HTML, PDF, DOCX).
• Quarto will render the document, and the output file will be saved in the same directory.

Example -1

Here’s an example of creating a bar plot using Seaborn package with a sample dataset:

# Import required libraries
import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = {
    "Category": ["A", "B", "C", "D", "E"],
    "Values": [23, 45, 56, 78, 12]
}

# Convert to a DataFrame
import pandas as pd
df = pd.DataFrame(data).sort_values(by = "Values", ascending=False)

# Create a bar plot
sns.barplot(x="Category", y="Values", data=df, palette="viridis")

# Add a title and labels
plt.title("Sample Bar Plot")
plt.xlabel("Category")
plt.ylabel("Values")
# Show the plot
plt.show()

Example -2

An example of creating a line graph using plotly package

import plotly.express as px

df = px.data.gapminder().query("country=='Canada'")
fig = px.line(df, x="year", y="lifeExp", title='Life expectancy in Canada')
fig.show()

Exploring Python documentation and community forums

Websites

• w3schools
• datacamp
• codeacademy
• learnpython

Forums

• Stack Overflow
• Real Python

THANK YOU !!!