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Lists — Python’s Workhorse

Lists are mutable, ordered collections and are the most commonly used data structure in Python.

# Creating and modifying lists
fruits = ["apple", "banana", "cherry"]
fruits.append("date")
fruits.insert(1, "blueberry")
fruits.remove("banana")
fruits.pop()
fruits.sort()
fruits.reverse()

# List slicing
first_three = fruits[:3]
last_two = fruits[-2:]
reversed = fruits[::-1]
every_other = fruits[::2]

Dictionaries — Key-Value Mapping

Dictionaries provide O(1) average lookup time and are foundational for real-world Python applications.

# Creating dictionaries
student = {
    "name": "Alice Johnson",
    "age": 20,
    "major": "Computer Science",
    "gpa": 3.8
}

# Accessing and modifying
name = student["name"]
age = student.get("age", 0)
student["year"] = "Junior"
student.update({"gpa": 3.9})

# Dictionary comprehension
squares_dict = {x: x**2 for x in range(10)}

# Nested dictionaries
university = {
    "students": {
        "001": {"name": "Alice", "gpa": 3.8},
        "002": {"name": "Bob", "gpa": 3.6}
    },
    "courses": {
        "CS101": {"name": "Intro to Programming", "credits": 3}
    }
}

Sets — Unique Collections

Sets automatically enforce uniqueness and support powerful mathematical operations.

# Set operations
set_a = {1, 2, 3, 4, 5}
set_b = {4, 5, 6, 7, 8}

union = set_a | set_b
intersection = set_a & set_b
difference = set_a - set_b
symmetric_diff = set_a ^ set_b

# Removing duplicates
numbers_with_duplicates = [1, 2, 2, 3, 3, 3, 4]
unique_numbers = list(set(numbers_with_duplicates))

Working with Files in Python

File I/O is fundamental for assignments, automation, and data processing.

# Reading files safely
with open('data.txt', 'r') as file:
    content = file.read()
    lines = file.readlines()
    for line in file:
        process(line)

# Writing files
with open('output.txt', 'w') as file:
    file.write("Hello, World!\n")
    file.writelines(["Line 1\n", "Line 2\n"])

# CSV files
import csv
with open('students.csv', 'r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row['name'], row['gpa'])

# JSON handling
import json
data = {"students": [{"name": "Alice", "gpa": 3.8}]}

with open('data.json', 'w') as file:
    json.dump(data, file, indent=2)

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

Exception Handling — Writing Robust Code

# Basic try-except
try:
    result = 10 / user_input
    file = open('important.txt', 'r')
except ZeroDivisionError:
    print("Cannot divide by zero!")
except FileNotFoundError:
    print("File not found!")
except Exception as e:
    print(f"Unexpected error: {e}")
else:
    print("Everything worked!")
finally:
    file.close()

# Custom exceptions
class InvalidGradeError(Exception):
    pass

def add_grade(grade):
    if not 0 <= grade <= 100:
        raise InvalidGradeError("Grade must be 0-100")

Python’s Essential Libraries

NumPy — Numerical Computing

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
zeros = np.zeros((3, 3))
ones = np.ones((2, 4))

squared = arr ** 2
normalized = (arr - arr.mean()) / arr.std()

product = np.dot(arr[:2], arr[:2])

Pandas — Data Analysis Powerhouse

import pandas as pd

df = pd.DataFrame({
    'student_id': [1, 2, 3, 4, 5],
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'gpa': [3.8, 3.6, 3.9, 3.5, 3.7],
    'major': ['CS', 'Math', 'CS', 'Physics', 'CS']
})

df = pd.read_csv('students.csv')
df.head()
df.describe()
cs_students = df[df['major'] == 'CS']

Matplotlib & Seaborn — Visualization

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 6))
plt.plot(x_data, y_data, label='Trend')
plt.scatter(x_data, y_data, alpha=0.5)
plt.legend()
plt.grid(True)
plt.show()

sns.histplot(data=df, x='gpa', bins=20, kde=True)
sns.boxplot(data=df, x='major', y='gpa')
sns.heatmap(correlation_matrix, annot=True)

from flask import Flask, render_template, request, jsonify

app = Flask(__name__)

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/api/students', methods=['GET', 'POST'])
def students():
    if request.method == 'POST':
        data = request.get_json()
        # Process student data
        return jsonify({"message": "Student created", "id": 123})
    else:
        # Return list of students
        return jsonify({"students": get_all_students()})

if __name__ == '__main__':
    app.run(debug=True)

# models.py
from django.db import models

class Student(models.Model):
    name = models.CharField(max_length=100)
    email = models.EmailField(unique=True)
    gpa = models.DecimalField(max_digits=3, decimal_places=2)
    enrollment_date = models.DateField(auto_now_add=True)

    class Meta:
        ordering = ['-gpa']

    def __str__(self):
        return self.name


# views.py
from django.shortcuts import render
from .models import Student

def student_list(request):
    students = Student.objects.all()
    return render(request, 'students/list.html', {
        'students': students
    })

def high_performers(request):
    students = Student.objects.filter(gpa__gte=3.5)
    return render(
        request,
        'students/high_performers.html',
        {'students': students}
    )

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report

# Load and prepare data
df = pd.read_csv('students.csv')
X = df[['study_hours', 'attendance', 'previous_gpa']]
y = df['passed']

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Feature scaling
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train model
model = LogisticRegression()
model.fit(X_train_scaled, y_train)

# Evaluate
y_pred = model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2%}")
print(classification_report(y_test, y_pred))

# Predict new data
new_student = [[25, 85, 3.2]]
new_student_scaled = scaler.transform(new_student)
prediction = model.predict(new_student_scaled)
probability = model.predict_proba(new_student_scaled)