Lesson 4 · Data analytics zero to hero

Python Lists, Tuples, Sets & Dicts Explained | Data Analytics #4

Video four of the 30-part series: lists, dictionaries, tuples, and sets, the containers every real dataset ends up living in before pandas takes over.…

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CourseData analytics zero to hero
Lesson4 of 30
Video11 min
FormatJupyter notebook · 10 code cells

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Data Analytics Zero to Hero, Video 4: Data Structures#

Video four of the 30-part series: lists, dictionaries, tuples, and sets, the containers every real dataset ends up living in before pandas takes over.
Builds on functions and control flow from videos two and three.
Let's jump straight in.

Before You Start#

Open a new Jupyter Notebook in VS Code and select your Python interpreter as the kernel.

Part 1: Lists#

car_models = ['Mazda RX4', 'Datsun 710', 'Hornet 4 Drive', 'Duster 360']
print(car_models)
print(len(car_models))
print(car_models[0])
print(car_models[-1])

['Mazda RX4', 'Datsun 710', 'Hornet 4 Drive', 'Duster 360']
4
Mazda RX4
Duster 360

car_models.append('Valiant')
print(car_models)
car_models.remove('Datsun 710')
print(car_models)
car_models[0] = 'Mazda RX4 Wag'
print(car_models)

['Mazda RX4', 'Datsun 710', 'Hornet 4 Drive', 'Duster 360', 'Valiant']
['Mazda RX4', 'Hornet 4 Drive', 'Duster 360', 'Valiant']
['Mazda RX4 Wag', 'Hornet 4 Drive', 'Duster 360', 'Valiant']

mpg_values = [21.0, 22.8, 21.4, 18.7, 18.1]
print(sorted(mpg_values))
print(sorted(mpg_values, reverse=True))
print(sum(mpg_values) / len(mpg_values))

[18.1, 18.7, 21.0, 21.4, 22.8]
[22.8, 21.4, 21.0, 18.7, 18.1]
20.4

Part 2: Dictionaries#

car = {'model': 'Mazda RX4', 'mpg': 21.0, 'cyl': 6, 'hp': 110}
print(car)
print(car['model'])
print(car['mpg'])

{'model': 'Mazda RX4', 'mpg': 21.0, 'cyl': 6, 'hp': 110}
Mazda RX4
21.0

car['transmission'] = 'manual'
print(car)
print(car.get('weight', 'unknown'))
print(car.keys())
print(car.values())

{'model': 'Mazda RX4', 'mpg': 21.0, 'cyl': 6, 'hp': 110, 'transmission': 'manual'}
unknown
dict_keys(['model', 'mpg', 'cyl', 'hp', 'transmission'])
dict_values(['Mazda RX4', 21.0, 6, 110, 'manual'])

A List of Dictionaries#

cars = [
    {'model': 'Mazda RX4', 'mpg': 21.0, 'cyl': 6},
    {'model': 'Datsun 710', 'mpg': 22.8, 'cyl': 4},
    {'model': 'Duster 360', 'mpg': 14.3, 'cyl': 8}
]
for car in cars:
    print(f"{car['model']}: {car['mpg']} mpg, {car['cyl']} cylinders")

Mazda RX4: 21.0 mpg, 6 cylinders
Datsun 710: 22.8 mpg, 4 cylinders
Duster 360: 14.3 mpg, 8 cylinders

Part 3: Tuples#

dimensions = (17.02, 2.62)
print(dimensions)
print(dimensions[0])
qsec, weight = dimensions
print(qsec, weight)

(17.02, 2.62)
17.02
17.02 2.62

try:
    dimensions[0] = 99
except TypeError as e:
    print('Error:', e)

Error: 'tuple' object does not support item assignment

Part 4: Sets#

cylinder_counts = [6, 4, 8, 6, 4, 4, 8, 6]
unique_cylinders = set(cylinder_counts)
print(unique_cylinders)
print(len(unique_cylinders))

{8, 4, 6}
3

four_cyl_models = {'Datsun 710', 'Merc 240D', 'Fiat 128'}
efficient_models = {'Datsun 710', 'Fiat 128', 'Toyota Corolla'}
print(four_cyl_models & efficient_models)
print(four_cyl_models | efficient_models)
print(four_cyl_models - efficient_models)

{'Fiat 128', 'Datsun 710'}
{'Fiat 128', 'Datsun 710', 'Merc 240D', 'Toyota Corolla'}
{'Merc 240D'}

Wrap-Up: What You Learned#

Lists: ordered and changeable, with indexing, append, remove, and sorting.
Dictionaries: key-value pairs, plus a list of dictionaries as a mental model for a full dataset.
Tuples: ordered but immutable, and how they relate to unpacking multiple return values.
Sets: unique, unordered values, plus intersection, union, and difference.
These four containers are the foundation pandas itself is built on top of. Video five covers reading and writing real CSV, text, and JSON files, so subscribe if you want it to land automatically.

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