Libraries & Frameworks

1. 📦 Install — download it once

pip install pandas — that's all you need to get started.

2. 📥 Import — bring it into your namespace

Whole module, single function, or with an alias: import pandas as pd

3. 📖 Read the docs & use it

Every library has docs with parameters, return values, examples. Bookmark them — then call its functions and classes like your own code.

import pandas as pd

df_cars = pd.read_csv("cars.csv")
print(df_cars)

    Make    Model  Year  Color  Mileage  Price
0  Toyota  Corolla  2020  Black    45000  15500
1   Honda    Civic  2018    Red    62000  13200
2  Toyota     RAV4  2022  White    21000  26800
3    Ford    Focus  2019  Black    78000   9900
4   Honda   Accord  2021  Black    32000  22100
5    Ford  Mustang  2023    Red     8500  41700

🔎 Useful next steps: df.head(), df.info(), df.describe().

Find black cars from 2020 onwards:

import pandas as pd

df_cars = pd.read_csv("cars.csv")

mask = (df_cars["Color"] == "Black") & (df_cars["Year"] >= 2020)
black_2020 = df_cars[mask]
print(black_2020)

    Make    Model  Year  Color  Mileage  Price
0  Toyota  Corolla  2020  Black    45000  15500
4   Honda   Accord  2021  Black    32000  22100

💡 Boolean indexing is vectorized — no Python loops, fast on millions of rows.

Average mileage and price per make:

avg = (df_cars
       .groupby("Make")
       .agg({"Mileage": "mean", "Price": "mean"})
       .rename(columns={
           "Mileage": "Avg Mileage",
           "Price": "Avg Price",
       }))
print(avg)

         Avg Mileage   Avg Price
Make
Ford         43250.0     25800.0
Honda        47000.0     17650.0
Toyota       33000.0     21150.0

📊 groupby().agg() is the SQL-ish workhorse of pandas. Built-in aggregations: mean, sum, min, max, count, std…

How many cars per color?

color_counts = df_cars["Color"].value_counts()
print(color_counts)
# Black    3
# Red      2
# White    1

Newest car per make:

newest = (df_cars
          .sort_values("Year", ascending=False)
          .groupby("Make", as_index=False)
          .first())
print(newest[["Make", "Model", "Year"]])
# Ford   Mustang  2023
# Honda  Accord   2021
# Toyota RAV4     2022

Calculate depreciation (10% per year) — add a new column:

YEAR_NOW = 2026

df_cars["Depreciation"] = (
    df_cars["Price"] * 0.1 * (YEAR_NOW - df_cars["Year"])
)
print(df_cars[["Make", "Model", "Year", "Price", "Depreciation"]])

Write the updated table to a new CSV:

df_cars.to_csv("updated_cars.csv", index=False)

Three commands — you have a running web app:

$ pip install django

$ django-admin startproject mysite

$ cd mysite
$ python manage.py startapp blog

$ python manage.py runserver
# Starting development server at http://127.0.0.1:8000/

Project structure that gets generated:

mysite/
├── manage.py            # CLI entrypoint
├── mysite/
│   ├── settings.py      # config
│   ├── urls.py          # URL routing
│   └── wsgi.py          # server hook
└── blog/
    ├── models.py        # <-- you
    ├── views.py         # <-- you
    └── templates/       # <-- you

Define your data as Python classes — Django writes the SQL:

# blog/models.py
from django.db import models

class Author(models.Model):
    name = models.CharField(max_length=100)

class Book(models.Model):
    title = models.CharField(max_length=200)
    author = models.ForeignKey(Author, on_delete=models.CASCADE)
    published = models.DateField()

Migrations & queries — SQL without writing SQL:

$ python manage.py makemigrations
$ python manage.py migrate

# In Python:
recent = Book.objects.filter(published__year=2026)
for book in recent:
    print(book.title, "—", book.author.name)

urls.py — map URLs to view functions:

from django.urls import path
from .views import book_list, book_detail

urlpatterns = [
    path("", book_list, name="book-list"),
    path("<int:book_id>/", book_detail, name="book-detail"),
]
# mounted at /books/ in the project urls.py

views.py — business logic for each URL:

from django.shortcuts import render
from .models import Book

def book_list(request):
    books = Book.objects.all()
    return render(request, "books.html", {"books": books})

👉 Note: Django calls book_list(request). You never invoke it — that's IoC.

templates/books.html — HTML with placeholders:

<!DOCTYPE html>
<html>
<head><title>Book List</title></head>
<body>
  <h1>Books</h1>
  <ul>
    {% for book in books %}
      <li>{{ book.title }} — {{ book.author.name }}</li>
    {% empty %}
      <li>No books yet.</li>
    {% endfor %}
  </ul>
</body>
</html>

📚 {{ … }} renders a value. {% … %} runs logic (loops, conditions). HTML is auto-escaped — XSS protection by default.