Summary
This chapter covers implementing code to compute antiderivatives (indefinite integrals) and definite integrals symbolically in Python with Sympy, evaluating symbolic expressions at numerical points, and printing the results for visualization.

General domain of usage
Mathematical analysis

## Computing an Indefinite Integral (Antiderivative)

An **indefinite integral** represents the **antiderivative** of a function. It finds the general form of a function whose derivative gives the original function.


import sympy as sp

# Define function
x = sp.Symbol('x')
f = x**2 

# Compute indefinite integral
F = sp.integrate(f, x) 

# Output: x**3 / 3 
print(F) 

## Computing a Definite Integral (Area Under Curve)
A definite integral finds the accumulated sum of a function over a range $$[a,b]$$.

import sympy as sp

# Define function
x = sp.Symbol('x')
f = x**2 

# Define integration limits
a, b = 0, 2

# Compute definite integral
integral_value = sp.integrate(f, (x, a, b)) 

# Output: 8/3 
print(integral_value)

## Common Integrals in Python

Python allows us to compute common mathematical integrals **symbolically**. Here are a few examples:

import sympy as sp

# Define function
x = sp.Symbol('x')

# Exponential integral
exp_integral = sp.integrate(sp.exp(x), x)  

# Sigmoid function integral
sigmoid_integral = sp.integrate(1 / (1 + sp.exp(-x)), x)  

# Quadratic function integral
quadratic_integral = sp.integrate(2*x, (x, 0, 2))

# Print results
print(exp_integral)          # Output: e^x  
print(sigmoid_integral)      # Output: log(1 + e^x)
print(quadratic_integral)    # Output: 4  

Master the mathematical foundations essential for data science. Explore core concepts in functions, calculus, linear algebra, probability, and dimensionality reduction. Build both theoretical understanding and practical coding experience to strengthen your ability to analyze data, model complex systems, and apply advanced techniques in machine learning.

Explore the foundation of mathematical functions. Learn different types of algebraic and transcendental functions, their properties, and how to implement them in Python to solve real-world problems.

Master the concepts of sets and series, from basic operations to practical applications. Gain hands-on experience implementing set operations and working with arithmetic and geometric series in Python.

Develop a solid understanding of limits, derivatives, integrals, and partial derivatives. Connect theory to practice by implementing these concepts in Python and applying them to optimization through gradient descent.

Build strong knowledge of vectors, matrices, and transformations. Learn decomposition methods and eigenvalue analysis, while reinforcing concepts with Python coding challenges and practical data science applications.

Dive into probability theory and statistics. Study conditional probability, Bayes' theorem, and statistical measures. Implement key concepts in Python, simulate distributions, and strengthen your skills through challenges and quizzes.

Implementing Integrals in Python

Computing an Indefinite Integral (Antiderivative)

Computing a Definite Integral (Area Under Curve)

Common Integrals in Python