In this tutorial, we will delve into the basics of plotting a linear equation in Python using a very popular mathematical plotting library known as Matplotlib.

This will provide you with knowledge that is foundational to data visualization, data science, machine learning and so many other areas where Python is instrumental.

### Step 1: Installing the Necessary Libraries

Before we can start plotting, we need to install the necessary libraries. **Matplotlib** and **NumPy** are the two main libraries we will use in this lesson.

They can be installed using pip, which is a package manager for Python. Run the following commands in your terminal to install them:

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pip install matplotlib numpy |

### Step 2: Importing the Libraries

Once we have installed the required libraries, we need to import them into our Python script. This is done using the **import** keyword.

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import matplotlib.pyplot as plt import numpy as np |

### Step 3: Defining the Linear Equation

Next, we need to define the linear equation we want to plot. For instance, if we choose to plot the function y = 3*x + 2, “x” is our independent variable, and “y” is the dependent variable.

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x = np.linspace(-10, 10, 400) y = 3 * x + 2 |

Here, we generate 400 equally spaced values for x ranging from -10 to 10 using **np.linspace** function.

### Step 4: Plotting the Equation

Now, we will utilize the **pyplot** module of Matplotlib to plot the graph of our equation. Here’s how:

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plt.plot(x, y) plt.show() |

## The Complete Python Code is:

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import matplotlib.pyplot as plt import numpy as np x = np.linspace(-10, 10, 400) y = 3 * x + 2 plt.plot(x, y) plt.show() |

## Conclusion

Successfully plotting the linear equation is as simple as that. With these steps, we have successfully exploited Python, specifically Matplotlib and NumPy libraries, to plot a linear equation.

This is just the tip of the iceberg as Python and its libraries provide wide functionalities for complex and robust mathematical computations and visualizations. To further your knowledge on this topic, visit the Matplotlib and Numpy documentation.