Matplotlib Basics — Part. 1 — Lines Plotting

Matplotlib is one of the most famous Python libraries. It is a graphics library used to make static, animated, and interactive visualizations.

I’ll try to teach you the basics of this library.

Getting Started

First, you have to install matplotlib . You can do it easily with pip :

pip install matplotlib

Now, you can import matplotlib this way:

import matplotlib.pyplot as plt

Most of the functions we’ll use are located in the pyplot package, but you should know there are other packages such as animation or backends . And most of the time, this package is imported as plt .

Let’s create a figure now:

plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()

Nice, it works!

Plotting

Depending on what we want to plot, we can use several functions. Here we’ve used plot , but we can also draw histograms, or bar charts. I’ll talk about these later, for now, let’s focus on plot .

plot is used to draw points on the chart. For example, above, we’ve drawn the points from (0, 1) to (3, 4). We can draw anything using points. For example, we can draw mathematical functions:

The first parameter you pass to plot is the x-axis. The second one is the y-axis. Then you can pass several parameters to customize the appearance.

For example, we can draw random data this way by passing random data to x and y:

plt.plot([random.randint(0, 100) for i in range(100)], [random.randint(0, 100) for i in range(100)])
plt.show()

We can also draw several lines by calling several times plot :

x1 = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]

x2 = [1, 2, 3, 4, 6]
y2 = [1, 8, 27, 64, 216]

plt.plot(x1, y1)
plt.plot(x2, y2)

plt.show()

The lines will automatically have random colors. Using this, we can make some beautiful plots:

def draw_random_walks(n, steps):
    for i in range(n):
        x = [0]
        y = [0]
        for j in range(steps):
            x.append(x[-1] + random.choice([-1, 1]))
            y.append(y[-1] + random.choice([-1, 1]))
        plt.plot(x, y)
    plt.show()


draw_random_walks(10, 1000)

It looks like a map from a video game!

Customization

There are many settings you can customize with Matplotlib. You can find many of them in the code below:

    x = [1, 2, 3, 4, 5]
    y = [1, 4, 9, 16, 25]
    plt.plot(x, y, color='red', marker='o', linestyle='dashed', linewidth=2, markersize=12, label='y = x^2', alpha=0.5,
             markeredgecolor='blue', markerfacecolor='yellow', markeredgewidth=2)

    plt.xlabel('x')
    plt.ylabel('y')
    plt.title('y = x^2')
    plt.legend()

    plt.xlim(0, 6)
    plt.ylim(0, 30)

    plt.xticks([1, 3, 5])
    plt.yticks([5, 15, 25])

    plt.show()

Here are some parameters you can pass to plot :

• color: to change the line’s color
• linestyle: to change the style of the line
• linewidth
• marker: to display markers at every point. There are many styles of markers (“o”, “x”, “^”, …)
• markersize
• markeredgecolor
• markerfacecolor
• label: the label to display in the legend for the line
• alpha: transparency

Then, you can customize other things such as the labels of the axis, the title of the plot, etc…

• xlabel: label of the x-axis
• ylabel
• title
• legend: to enable the legend
• xlim: limit values of the x-axis
• ylim
• xticks: ticks of the x-axis
• yticks

Below is the plot you get from the code above:

You can also add a grid to the plot using plt.grid()

Subplots

A figure is a set of subplots. It means you can create figures with many charts.

To do this, you have to use subplot :

    subplot1 = plt.subplot(2, 1, 1)
    subplot1.plot([1, 2, 3, 4], [1, 4, 9, 16], color='red', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=12)
    subplot1.set_title('Plot 1')

    subplot2 = plt.subplot(2, 1, 2)
    subplot2.plot([1, 2, 3, 4], [1, 4, 9, 16], color='green', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=12)
    subplot2.set_title('Plot 2')

    plt.show()

You can create subplots with plt.subplot . The first number is the number of rows in the figure, then the number of columns, and finally the number of the subplot you want to retrieve.

You can work on each subplot as if it was a unique plot.

You can create as many subplots as you want:

    rows = 5
    cols = 5

    for i in range(rows):
        for j in range(cols):
            plt.subplot(rows, cols, i * cols + j + 1)
            plt.plot([x**(i*cols+j+1) for x in range(10)])
            plt.axis('off')

    plt.show()

Final Note

I’ve covered just the basics, but it’s enough to have some fun with Matplotlib!

There are still a lot of things to say about Matplotlib. Some of the most important features are those that allow you to make bar charts, pie charts, scatter plots, etc…

I will talk about these in another story, so be sure to follow me if you don’t want to miss the next Matplotlib article!

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