How to Compute Power-Rule Derivatives in Python with SymPy (in 75 seconds)

Master the Power Rule for differentiation in Python using the SymPy library! This tutorial walks you through the process of computing derivatives symbolically with just a few lines of code. If you're learning calculus, working on machine learning models, or doing mathematical research, this method is fast, efficient, and scalable.

✅ What’s Covered:

Importing the sympy library for symbolic math
Defining symbolic variables with symbols()
Using diff() to compute derivatives
Differentiating polynomial functions using the Power Rule
🔗 Why This Video?
The Power Rule is one of the most essential differentiation techniques, and Python makes it incredibly easy to apply to both simple and complex functions. Whether you're in high school calculus, university math, or data science, this quick guide will get you started with symbolic differentiation in Python!

🎯 Who Is This For?

Python programmers looking to apply calculus concepts
Data scientists and engineers needing symbolic differentiation
Students learning derivatives in calculus
Anyone working with machine learning, finance, or optimization
💻 Code from Tutorial:
***
from sympy import symbols, diff

# Define a symbolic variable
x = symbols('x')

# Compute the derivative of x^5
f = x**5
diff(f, x)

# Compute the derivative of a polynomial function
f = 3 * x**4 + x**3 - 5 * x**2 + 9 * x - 9
diff(f, x)
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