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Why Python?

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This crash course will teach you the basics of the Python programming language.

Motivation

lattenspitze code
lattenspitze plotly
Creation of a 3D surface plot of the Lattenspitze. 🏔️
That's the power of Python - ease of use paired with a wide range of functionalities stemming from a large developer community! 🦾

  • Ease of use

    Python with its syntax is easy to learn and yet very powerful.

  • Flexible

    Python is a versatile language that can be used for web development, data analysis, artificial intelligence, and many more.

The below section should give you an impression of what you can do with Python. This is not an extensive list by all means. It might sound trashy but if you can imagine something you probably can build it in Python.

Don't worry about the code snippets too much, after finishing the course you'll have a better understanding of them and will be able to run and modify code yourself. For now, the snippets illustrate the capabilities of the language and what complex things you can achieve with little code.

Examples

Visualizations

You can create stunning and interactive visualizations1.

# Source: https://plotly.com/python/tile-county-choropleth/
import plotly.express as px

df = px.data.election()
geojson = px.data.election_geojson()

fig = px.choropleth_map(
    df,
    geojson=geojson,
    color="Bergeron",
    locations="district",
    featureidkey="properties.district",
    center={"lat": 45.5517, "lon": -73.7073},
    map_style="carto-positron",
    zoom=9,
)
fig.show()

Machine Learning/AI

... or you can easily train your own machine learning models. In this example, with just a few lines of code, a decision tree is fit and visualized2.

# Source: https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#train-tree-classifier
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, plot_tree

iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

clf = DecisionTreeClassifier(max_leaf_nodes=3, random_state=0)
clf.fit(X_train, y_train)

plot_tree(clf)
plt.savefig("tree.svg")
Decision Tree

Automation

... or automate tasks that you would have otherwise done manually. This code snippet fetches some data (from an online service) and writes it to a file3.

import json
from pathlib import Path

import httpx

url = "https://pokeapi.co/api/v2/pokemon/charmander"

response = httpx.get(url)

with Path("charmander.json").open("w") as file:
    json.dump(response.json(), file, indent=4)

Web Development

You can create websites, just like this one. In fact, all the heavy lifting of this site is done by Python and tools developed by its community.

Getting Started...

In the next sections, we will install Python including the code editor Visual Studio Code.

Info

Both Python and Visual Studio Code are already pre-installed on PCs in the MCI computer rooms. If you are working with your own computer, please proceed to the installation page.

  1. Plotly is a Python graphing library that lets you create interactive, publication-quality graphs. ↩

  2. Scikit-learn is a Python library for machine learning. ↩

  3. HTTPX is a Python library to interact with APIs. ↩