Alright, you've set up your environment and learned how to import the necessary libraries. Now, let's put that into practice and create our very first visualization using Matplotlib. This will be a straightforward line plot, serving as a foundation for the more sophisticated plots we'll build later.

Remember from the previous section, we typically import Matplotlib's pyplot module like this:

import matplotlib.pyplot as plt
import numpy as np # Often used for generating sample data

Our goal is to visualize a simple relationship, for instance, how something changes over a sequence of steps. Let's imagine we have some data points. We can represent these points using Python lists or NumPy arrays. For this example, let's use simple lists:

# Sample data: X values (e.g., time points or sequence)
x_values = [0, 1, 2, 3, 4, 5]

# Sample data: Y values (e.g., measurements corresponding to x values)
y_values = [0, 1, 4, 9, 16, 25] # Notice these are squares of x_values

Here, x_values represents the horizontal axis coordinates, and y_values represents the vertical axis coordinates. Each corresponding pair (like (0, 0), (1, 1), (2, 4), etc.) defines a point on our graph.

Now, let's use Matplotlib to plot these points and connect them with a line. The primary function for this is plt.plot():

# 1. Create the plot
plt.plot(x_values, y_values)

# 2. Add labels and title for clarity
plt.xlabel("X Axis Label")
plt.ylabel("Y Axis Label")
plt.title("My First Simple Plot")

# 3. Display the plot
plt.show()

Let's break down this code:

plt.plot(x_values, y_values): This is the core command. We pass our horizontal (x_values) and vertical (y_values) data to the plot function. Matplotlib uses this information to draw points and connect them with lines by default.
plt.xlabel(...), plt.ylabel(...), plt.title(...): While not strictly necessary to show a plot, adding labels to the axes and a title to the plot is fundamental for making visualizations understandable. These functions add descriptive text to the corresponding parts of the graph. We'll explore customization options in detail later.
plt.show(): This command tells Matplotlib, "Okay, I'm done defining the plot, now render and display it." Without this (in many environments, like standard Python scripts), you might define a plot, but nothing will appear on your screen. In interactive environments like Jupyter notebooks, the plot might appear automatically after the cell execution, but explicitly using plt.show() is good practice.

When you run this complete script, you should see a window pop up (or the plot appear directly in your notebook) displaying a graph. This graph will have the numbers 0 through 5 on the x-axis and 0 through 25 on the y-axis. You'll see a line starting at (0,0), going up to (1,1), then (2,4), and so on, ending at (5,25), forming a curve. The labels "X Axis Label", "Y Axis Label", and the title "My First Simple Plot" will also be displayed.

Congratulations! You've successfully created your first data visualization in Python using Matplotlib. Even this simple example demonstrates the basic workflow: prepare data, use a plotting function, add descriptive elements, and display the result. In the following chapters, we will build upon these steps to create more varied and informative plots.