Tutorial – Matplotlib Line Plot – Shiksha Online

Line Plot is one of the easiest and most basic graphical analysis techniques that play an important role in data analysis when working on machine learning or data science projects. They are used to express a relationship between two variables. The article focuses on plotting a line chart in Python using Matplotlib.

Python’s most popular visualization library – Matplotlib, provides support for many useful graphical visualizations. For this article, we are going to focus on plotting a line chart in Python using Matplotlib.

We will be covering the following sections:

• Quick Introduction to Line Plots
• Installing and Importing Matplotlib
• Creating a Matplotlib Line Plot
• Adding Elements to the Line Plot
• Parameters of the Line Plot
• Saving your Line Plot
• Endnotes

Quick Introduction to Line Plots

A line plot visualizes information as a series of data points called markers connected by straight line segments. These are also used to express trends in data over intervals of time. Line Plots display a numerical variable on one axis and a categorical variable on the other.

Let’s take a fun little example to understand how to work with line plots. Suppose you’ve been given the maximum recorded temperature around your city for the last three weeks:

Installing and Importing Matplotlib

First, let’s install the Matplotlib library in your working environment. Execute the following command in your terminal:

Now let’s import the libraries we’re going to need today:

In Matplotlib, pyplot is used to create figures and change their characteristics.

The %matplotlib inline function allows for plots to be visible when using Jupyter Notebook.

Creating a Matplotlib Line Plot

Load the dataset

Plotting the data

To plot a line chart, we use the generic plot() function:

Let’s add a few elements here to help us interpret the visualization better. 

Adding Elements to the Line Plot

The plot we have created would not be easily understandable to a third pair of eyes without context, so let’s try to add different elements to make it more readable:

• Use plt.title() for setting a plot title
• Use plt.xlabel() and plt.ylabel() for labeling x and y-axis respectively
• Use plt.legend() for the observation variables
• Use plt.show() for displaying the plot

Through the above plot, we can say that the temperatures were the highest during mid-week.

Parameters of the Line Plot

Let’s plot for all three weeks within the same axis first:

Through the above plot, we can say that the temperatures have dipped during the third week, being particularly low on the Wednesday of week 3.

Note: To plot multiple line charts separate axis, just add plt.show() after each plt.plot() function.

• Use parameter color or c to specify the line colors:

• Use parameter linestyle or ls to specify line styles:

• Use parameter linewidth or lw to highlight one line in the chart:

• Use alpha parameter to specify the transparency of a line. It takes a scalar value between 0 and 1:

• Use parameter marker or to specify marker style string:

(‘o’, ‘v’, ‘^’, ‘<‘, ‘>’, ‘8’, ‘s’, ‘p’, ‘*’, ‘h’, ‘H’, ‘D’, ‘d’, ‘P’, ‘X’)

• Use mfc parameter to highlight the markers with the specified color
• Use mec parameter to highlight the marker edges with the specified color
• Use ms parameter to specify the marker size

Saving your Line Plot

You can save your plot as an image using the savefig() function. Plots can be saved in – .png, .jpeg, .pdf, and many other supporting formats. 

Let’s try saving the ‘Line Plot of Temperatures for 3 Weeks’’ plot we have created above:

The image would have been saved with the filename ‘lineplot.png’. 

To view the saved image, we’ll use the matplotlib.image module, as shown below:

Endnotes

The line plot is often viewed as a time series graph and is one of the most popular graphs in quantitative univariate analysis. Matplotlib is one of the oldest Python visualization libraries and provides a wide variety of charts and plots for better analysis. We hope this article on Matplotlib Line Plot helped you understand the concepts better.

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