Creating simple plots is a common step in data visualization. These visual representations help us to understand trends, patterns and relationships within data. Matplotlib is one of the most popular plotting libraries in Python which makes it easy to generate high-quality graphs with just a few lines of code. In this article, we'll see how to create basic plots using Matplotlib.

Before we start creating plots we need to install Matplotlib. We can install it using below command:

pip install matplotlib

Let's see an example of creating a Simple Plot

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

plt.plot(x, y)

plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Plot')

plt.show()

Output:

Syntax:

plt.plot(x, y, format_string, **kwargs)

Parameters:

• x: A sequence of values to be plotted along the x-axis.
• y: A sequence of values to be plotted along the y-axis.
• format_string: A string that specifies the line style, color and markers. It is a optional parameter.
• **kwargs: Other optional parameters such as label, linewidth, markersize, etc.

Key Matplotlib Functions for Simple Plots

Lets see various examples for customizing plots by using different functions.

Example 1: Creating a Customized Line Plot with Multiple Series

In this example, we’ll create a line plot with two different functions: y = x^2 and y = 30 - x^2. We will also customize the plot by adding gridlines, labels, title and legend.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [25, 20, 15, 10, 5]

plt.plot(x, y1, label='y = x^2', color='green', linestyle='--', marker='o')
plt.plot(x, y2, label='y = 30 - x^2', color='red', linestyle='-', marker='x')

plt.grid(True)

plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Customized Line Plot with Multiple Series')

plt.legend()
plt.savefig('all_features_plot.png')
plt.show()

Output:

Example 2: Creating Multiple Subplots in One Figure

We will create four different subplots in a 2x2 grid. Each subplot will display a different dataset. This technique is useful when we want to compare multiple datasets side by side in a single figure.

import matplotlib.pyplot as plt

a = [1, 2, 3, 4, 5]
b = [0, 0.6, 0.2, 15, 10, 8, 16, 21]
c = [4, 2, 6, 8, 3, 20, 13, 15]

fig = plt.figure(figsize =(10, 10))

sub1 = plt.subplot(2, 2, 1)
sub2 = plt.subplot(2, 2, 2)
sub3 = plt.subplot(2, 2, 3)
sub4 = plt.subplot(2, 2, 4)

sub1.plot(a, 'sb')

sub1.set_xticks(list(range(0, 10, 1)))
sub1.set_title('1st Rep')

sub2.plot(b, 'or')

sub2.set_xticks(list(range(0, 10, 2)))
sub2.set_title('2nd Rep')

sub3.plot(list(range(0, 22, 3)), 'vg')
sub3.set_xticks(list(range(0, 10, 1)))
sub3.set_title('3rd Rep')

sub4.plot(c, 'Dm')

sub4.set_yticks(list(range(0, 24, 2)))
sub4.set_title('4th Rep')

plt.show()

Output:

Example 3: Plotting a Scatter Plot

Scatter plots are basic type of plot used to visualize the relationship between two variables. This is useful when we want to see how one variable is related to another or look for correlations.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.scatter(x, y, color='blue', marker='x')

plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Scatter Plot')

plt.show()

Output:

With these simple techniques and examples we're now ready to start visualizing our data effectively using Matplotlib whether we're working with line plots, subplots or scatter plots.