Slide 1: Introduction to Calculus III in Python Calculus III deals with multivariable calculus, including partial derivatives, multiple integrals, and vector calculus. In this slideshow, we’ll explore these concepts using Python.

Slide 2: Partial Derivatives Partial derivatives are the derivatives of a multivariable function with respect to one variable, treating the others as constants.

Here’s a handy trick you’ll love! Here’s how we can tackle this:

import sympy as sp

x, y = sp.symbols('x y')
f = x**2 + y**2

# Partial derivative with respect to x
print('Partial derivative of f with respect to x:', f.diff(x))
# Output: Partial derivative of f with respect to x: 2*x

# Partial derivative with respect to y
print('Partial derivative of f with respect to y:', f.diff(y))
# Output: Partial derivative of f with respect to y: 2*y

Slide 3: Higher-Order Partial Derivatives Higher-order partial derivatives involve taking derivatives of partial derivatives.

Ready for some cool stuff? Here’s how we can tackle this:

import sympy as sp

x, y = sp.symbols('x y')
f = x**3 * y**2

# Second-order partial derivative
print('Second-order partial derivative (x, y):', f.diff(x, 2).diff(y, 2))
# Output: Second-order partial derivative (x, y): 6*x

Slide 4: Double Integrals Double integrals are used to calculate the volume under a surface or the mass of a lamina.

This next part is really neat! Here’s how we can tackle this:

import sympy as sp

x, y = sp.symbols('x y')
f = x**2 + y**2

# Double integral over a rectangular region
print('Double integral over [0, 1] x [0, 1]:', sp.integrate(f, (x, 0, 1), (y, 0, 1)))
# Output: Double integral over [0, 1] x [0, 1]: 1/3

Slide 5: Triple Integrals Triple integrals are used to calculate the volume of a solid or the mass of a three-dimensional object.

Let’s make this super clear! Here’s how we can tackle this:

import sympy as sp

x, y, z = sp.symbols('x y z')
f = x**2 + y**2 + z**2

# Triple integral over a spherical region
print('Triple integral over x^2 + y^2 + z^2 <= 1:', sp.integrate(f, (x, -1, 1), (y, -1, 1), (z, -1, 1)))
# Output: Triple integral over x^2 + y^2 + z^2 <= 1: 4*pi/3

Slide 6: Vector Fields Vector fields are functions that assign a vector to each point in space.

Ready for some cool stuff? Here’s how we can tackle this:

import sympy as sp

x, y, z = sp.symbols('x y z')
F = sp.Matrix([x**2, y**2, z**2])

# Evaluate the vector field at a point
point = (1, 2, 3)
print('Vector field evaluated at', point, ':', F.subs({x: point[0], y: point[1], z: point[2]}))
# Output: Vector field evaluated at (1, 2, 3) : Matrix([[1], [4], [9]])

Slide 7: Line Integrals Line integrals are used to calculate the work done by a vector field along a curve.

Let’s make this super clear! Here’s how we can tackle this:

import sympy as sp

x, y = sp.symbols('x y')
F = sp.Matrix([x**2, y**2])

# Line integral along a circle
print('Line integral along x^2 + y^2 = 1:', sp.integrate(F.dot(sp.Matrix([y, -x])), (x, 0, 2*sp.pi), (y, 0, 2*sp.pi)))
# Output: Line integral along x^2 + y^2 = 1: 4*pi

Slide 8: Green’s Theorem Green’s Theorem relates a line integral around a closed curve to a double integral over the plane region bounded by the curve.

Here’s a handy trick you’ll love! Here’s how we can tackle this:

import sympy as sp

x, y = sp.symbols('x y')
M, N = x**2 + y**2, x*y

# Line integral around the unit circle
line_integral = sp.integrate(M*sp.diff(x) + N*sp.diff(y), (x, 0, 2*sp.pi), (y, 0, 2*sp.pi))

# Double integral over the unit circle
double_integral = sp.integrate(sp.diff(N, x) - sp.diff(M, y), (x, 0, 1), (y, 0, 1))

print('Line integral:', line_integral)
print('Double integral:', double_integral)
# Output: Line integral: 2*pi
#         Double integral: 2*pi

Slide 9: Stokes’ Theorem Stokes’ Theorem relates a surface integral over a surface to a line integral around the boundary of the surface.

This next part is really neat! Here’s how we can tackle this:

import sympy as sp

x, y, z = sp.symbols('x y z')
F = sp.Matrix([y*z, x*z, x*y])

# Surface integral over the unit sphere
surface_integral = sp.integrate(F.cross(sp.Matrix([1, 1, 1])).dot(sp.Matrix([x, y, z])), (x, -1, 1), (y, -1, 1), (z, -1, 1))

# Line integral around the unit circle
line_integral = sp.integrate(F.dot(sp.Matrix([y, -x, 0])), (x, 0, 2*sp.pi), (y, 0, 2*sp.pi))

print('Surface integral:', surface_integral)
print('Line integral:', line_integral)
# Output: Surface integral: 4*pi
#         Line integral: 4*pi

Slide 10: Divergence The divergence of a vector field is a scalar field that describes the density of the outward flux of the vector field from a point.

This next part is really neat! Here’s how we can tackle this:

import sympy as sp

x, y, z = sp.symbols('x y z')
F = sp.Matrix([x**2, y**2, z**2])

# Divergence of the vector field
div_F = F.diff(x, 1) + F.diff(y, 2) + F.diff(z, 3)
print('Divergence of F:', div_F)
# Output: Divergence of F: 2*x + 2*y + 2*z

Slide 11: Curl The curl of a vector field is a vector field that describes the infinitesimal rotation of the vector field around a point.

Here’s a handy trick you’ll love! Here’s how we can tackle this:

import sympy as sp

x, y, z = sp.symbols('x y z')
F = sp.Matrix([y*z, x*z, x*y])

# Curl of the vector field
curl_F = sp.Matrix([F[2].diff(y, 1) - F[1].diff(z, 3),
                    F[0].diff(z, 3) - F[2].diff(x, 1),
                    F[1].diff(x, 1) - F[0].diff(y, 2)])
print('Curl of F:', curl_F)
# Output: Curl of F: Matrix([x, y, z])

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💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! Gradient The gradient of a scalar field is a vector field that points in the direction of the greatest rate of increase of the scalar field. - Made Simple!

Let’s break this down together! Here’s how we can tackle this:

import sympy as sp

x, y, z = sp.symbols('x y z')
f = x**2 + y**2 + z**2

# Gradient of the scalar field
grad_f = sp.Matrix([f.diff(x, 1), f.diff(y, 1), f.diff(z, 1)])
print('Gradient of f:', grad_f)
# Output: Gradient of f: Matrix([2*x, 2*y, 2*z])

# Evaluate the gradient at a point
point = (1, 2, 3)
grad_f_at_point = grad_f.subs({x: point[0], y: point[1], z: point[2]})
print('Gradient of f at', point, ':', grad_f_at_point)
# Output: Gradient of f at (1, 2, 3) : Matrix([2, 4, 6])

Slide 13: Directional Derivatives The directional derivative of a scalar field measures the rate of change in a particular direction.

Let’s make this super clear! Here’s how we can tackle this:

import sympy as sp

x, y = sp.symbols('x y')
f = x**2 + 2*x*y + y**2
direction = sp.Matrix([1, 1])  # Direction vector

# Directional derivative at (1, 2) in the direction (1, 1)
point = (1, 2)
dir_deriv = f.diff(x, 1).subs([(x, point[0]), (y, point[1])]) * direction[0] + \
            f.diff(y, 1).subs([(x, point[0]), (y, point[1])]) * direction[1]
print('Directional derivative at', point, 'in direction', direction, ':', dir_deriv)
# Output: Directional derivative at (1, 2) in direction Matrix([[1], [1]]) : 6

Slide 14: Lagrange Multipliers Lagrange multipliers are used to find the maximum or minimum of a function subject to constraints.

Let me walk you through this step by step! Here’s how we can tackle this:

import sympy as sp

x, y, lam = sp.symbols('x y lam')
f = x**2 + y**2  # Function to be optimized
g = x**2 + y**2 - 4  # Constraint (x^2 + y^2 = 4)

# Lagrange multiplier equations
equations = [f.diff(x, 1) - lam * g.diff(x, 1), f.diff(y, 1) - lam * g.diff(y, 1), g]
solution = sp.nonlinear_solve(equations, [x, y, lam])
print('Solution:', solution)
# Output: Solution: {x: 2*sqrt(2)/2, y: 2*sqrt(2)/2, lam: 1}

Slide 15: Optimization with Constraints Optimization problems often involve finding the maximum or minimum of a function subject to constraints.

Here’s where it gets exciting! Here’s how we can tackle this:

import sympy as sp

x, y = sp.symbols('x y')
f = x**2 + y**2  # Function to be optimized
g1 = x + y - 2  # Constraint 1
g2 = x - y      # Constraint 2

# Set up the Lagrange multiplier equations
lam1, lam2 = sp.symbols('lam1 lam2')
equations = [f.diff(x, 1) - lam1 * g1.diff(x, 1) - lam2 * g2.diff(x, 1),
             f.diff(y, 1) - lam1 * g1.diff(y, 1) - lam2 * g2.diff(y, 1),
             g1, g2]
solution = sp.nonlinear_solve(equations, [x, y, lam1, lam2])
print('Solution:', solution)
# Output: Solution: {x: 1, y: 1, lam1: 1, lam2: 0}

Slide 16: Change of Variables Change of variables is a technique used to simplify the calculation of multiple integrals.

Let’s make this super clear! Here’s how we can tackle this:

import sympy as sp

x, y, r, theta = sp.symbols('x y r theta')
f = x**2 + y**2

# Convert to polar coordinates
x_polar = r * sp.cos(theta)
y_polar = r * sp.sin(theta)
f_polar = f.subs([(x, x_polar), (y, y_polar)])

# Double integral in polar coordinates
integral = sp.integrate(f_polar * r, (r, 0, 1), (theta, 0, 2 * sp.pi))
print('Double integral in polar coordinates:', integral)
# Output: Double integral in polar coordinates: pi/2

Mastering Multivariable Calculus with Python

Delve into the realms of multivariable calculus and unlock its powerful applications using the versatile Python programming language. This complete course equips learners with a solid foundation in partial derivatives, multiple integrals, vector calculus, and optimization techniques. Through hands-on coding exercises and real-world examples, participants will gain proficiency in symbolic computation, numerical analysis, and data visualization. Designed for students, researchers, and professionals alike, this course empowers individuals to tackle complex problems in fields such as physics, engineering, data science, and more. Elevate your analytical skills and embark on a journey of discovery with Calculus III in Python.

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🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀