Python introduction

Ludovic Deneuville

History

• Created by Guido van Rossum (Netherlands)
• First version in 1991
• Managed by the Python Software Foundation since 2001
• Python 3.13: python --version

• Created at the end of 1989 during the Christmas holidays
• Python Software Foundation (non-profit organization): promote and protect the language in order to expand the community

Why Learn Python?

• Versatile and popular language
• Easy to learn and read
• Large community and many resources
• Many packages

• Important in the world of data science and engineering.

Python Fundamentals

• Interpreted and interactive language
• Dynamic typing
• Clear and concise syntax
• Supports several paradigms (procedural, object-oriented, functional)

• Explain the difference between an interpreted and a compiled language.
• Interactive: an environment where you can enter Python commands one by one and immediately see the results (Notebook)
• Dynamic typing (type checking at runtime)

a = 5
a = "toto"

Python at ENSAI

• CPython distribution
• Version 3.10 (python --version)
• You will code in:
• Jupyter notebooks
• Visual Studio Code
• Pre-installed packages

Distribution: a pre-packaged version of the Python interpreter including many modules, libraries, and tools

• Anaconda (data science and machine learning)
• Miniconda (lighter)

Other IDEs: PyCharm, Atom

Package manager pip, to be run in a terminal

Packages

In a terminal (for example: Git Bash)

pip list                            # Installed packages
pip install <package>               # Install a package
pip install <package>==<version>    # Specific version
pip uninstall <package>

# Distribution used

python -c 'import platform; print(platform.python_implementation())'

For projects:

• list of packages in a text file at the root
• pip install - r requirements.txt

Demo: run Python in the terminal

Popular Packages

Data science:

• NumPy
• Pandas, Polars
• Matplotlib, Seaborn

• NumPy
  • Numerical computation
  • ndarray, performance, statistics, array manipulation
• Pandas
  • dataframe, data analysis
• Matplotlib: charts

Writing Your First Python Code

• Variables and data types (int, float, str, bool, list, dict)
• Basic operations
• Sensitive to indentation
• Control structures (if, for, while)

• = : assignment
• == : equality test / != difference

Indentation: use a formatter in VSCode

Control Structures

• If
• For
• While
• Enumerate

#| eval: true
#| echo: true
note = 18

if note > 16:
print("Validated with honors")
elif note > 10:
print("Validated")
else:
print("Not validated")

#| eval: true
#| echo: true
for i in range(1, 5):
print(i)

#| eval: true
#| echo: true
cpt = 10
while cpt > 0:
print(cpt, end = " ")
cpt -= 1
print("boom")

#| eval: true
#| echo: true
liste = ["Alban", "Bertille", "Corine"]

for i, v in enumerate(liste):
print(f"index {i} : value {v}")

Collections - the 2 main ones

• List: ordered collection of modifiable values

list = ["apple", "pear", 2, True]

• Dictionary: unordered collection of key-value pairs

recipe = {"strawberry": 5, "Mustard": "5g"}

Other Useful Collections

• Set: unordered collection of unique values

primes = {2, 3, 5, 7, 11, 13}

• Tuple: ordered collection of immutable values

coord = (-1, 5, 4)

Note

Many other collection types exist (Tree, Linked List…).

Main Collections - Summary

Data Type	Ordered	Mutable	Duplicate
list	✔	✔	✔
dict		✔
tuple	✔		✔
set		✔

Development Assistance

To learn the language, avoid rushing to LLMs.

Prefer:

• Online documentation
• Your favorite search engine
• Stack Overflow

Useful Resources

• Real Python
• Python for Data Science
• Automate the Boring Stuff with Python

LLMs are not suitable for memorization but are useful for:

• debugging
• documenting

At Work

5 notebooks are available to help you learn the basics of the language:

How to use notebooks