Python Data Structures and File Operations Tutorial

Python provides several built-in data structures, each with specific uses and characteristics.

1. List (List)

Characteristics: ordered, mutable, allows duplicate elements.

Operations:

Creating a list:

fruits = ["apple", "banana", "cherry"]

Accessing elements:

print(fruits[0])  # Output: apple

Adding elements:

fruits.append("orange")  # Add to end
fruits.insert(1, "grape")  # Insert at position

Removing elements:

fruits.remove("banana")  # Remove first match
del fruits[0]  # Delete by index

Iterating:

for fruit in fruits:
    print(fruit)

2. Dictionary (Dictionary)

Characteristics: unordered (insertion order preserved in Python 3.7+), stores key‑value pairs.

Operations:

Creating a dictionary:

person = {"name": "Alice", "age": 30, "city": "New York"}

Accessing values:

print(person["name"])  # Output: Alice
print(person.get("age"))  # Output: 30

Updating or adding:

person["age"] = 31  # Update existing key
person["job"] = "Engineer"  # Add new key‑value

Deleting:

del person["city"]  # Remove key 'city'

Iterating:

for key, value in person.items():
    print(f"{key}: {value}")

3. Set (Set)

Characteristics: unordered, no duplicate elements.

Operations:

Creating a set:

colors = {"red", "green", "blue"}

Adding elements:

colors.add("yellow")

Removing elements:

colors.remove("red")  # Raises KeyError if missing
colors.discard("purple")  # No error if missing

Set operations:

set1 = {1, 2, 3}
set2 = {2, 3, 4}
union_set = set1.union(set2)          # Union
intersection_set = set1.intersection(set2)  # Intersection
difference_set = set1.difference(set2)      # Difference

4. Tuple (Tuple)

Characteristics: ordered, immutable.

Operations:

Creating a tuple:

coordinates = (10.0, 20.0)

Accessing elements:

print(coordinates[0])  # Output: 10.0

Iterating:

for coord in coordinates:
    print(coord)

Note: Tuples are immutable; to change, reassign the whole tuple.

File Operations

Python provides simple tools for reading, writing, and managing files.

1. Text Files

Reading:

with open('example.txt', 'r') as file:
    content = file.read()
    print(content)

Writing:

with open('output.txt', 'w') as file:
    file.write("Hello, World!\n")
    file.write("This is a new line.")

Appending:

with open('output.txt', 'a') as file:
    file.write("\nAdding more text.")

2. CSV Files

Using the csv module.

Reading:

import csv
with open('data.csv', newline='') as csvfile:
    reader = csv.reader(csvfile)
    for row in reader:
        print(', '.join(row))

Writing:

import csv
data = [
    ['Name', 'Age', 'City'],
    ['Alice', 30, 'New York'],
    ['Bob', 25, 'Los Angeles']
]
with open('output.csv', 'w', newline='') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerows(data)

3. JSON Files

Using the json module.

Reading:

import json
with open('data.json', 'r') as file:
    data = json.load(file)
    print(data)

Writing:

import json
data = {
    "name": "Alice",
    "age": 30,
    "city": "New York"
}
with open('output.json', 'w') as file:
    json.dump(data, file, indent=4)  # Indented for readability

Summary

Through the above content, you can see that Python provides rich and easy‑to‑use data structures and file‑operation methods. Mastering these basics helps you write code more efficiently and handle various data and file types with ease. If you have further questions or need additional help, feel free to ask!