Summary  
This chapter demonstrates how to define and implement exponential and logarithmic functions in Python using NumPy for computation (np.exp and np.log with a change-of-base formula), generate input values, and visualize the resulting curves with Matplotlib.  

General domain of usage  
Finance

## Exponential Function
Exponential functions model rapid growth or decay, commonly used in population modeling, finance, and physics. This function is of the form $$f(x) = ae^{bx}$$.

### Code Breakdown
- Generates `x` values between `-5` and `5`;
- Defines `exponential_function(x, a, b)`, where `a` scales the function, and `b` controls the growth rate;
- Plots the graph with **arrows** at both ends to show continuous growth;
- Marks the **y-intercept** at `x = 0` for clarity.


## Logarithmic Function

Logarithms are the inverse of exponentials, useful in scaling data and measuring natural growth processes. This function is defined as $$f(x) = \log_2(x)$$, meaning it calculates the power to which $$2$$ must be raised to obtain $$x$$.

### Code Breakdown
- Generates `x` values between `0.1` and `10` (to avoid `log(0)`, which is undefined);
- Defines `logarithmic_function(x, base=2)`, ensuring base `2` is used throughout;
- The graph includes an **arrow at the right end**, indicating it continues indefinitely;
- The **x-intercept** is marked at `x = 1`, where `log_2(1) = 0`.


Which base is used in the logarithmic function in this code?

This course distills the essentials of Python's math module, focusing on trigonometric functions, logarithmic operations, and mathematical constants. Learners will gain hands-on experience using Python to perform these standard mathematical computations, ideal for beginners seeking practical skills with the math module.

Implementing Exponential-Log Functions in Python

Exponential Function

Code Breakdown

Logarithmic Function

Code Breakdown