Matplotlib Markers

Matplotlib, a Python visualization library, is a great choice for 2D array charts. The Matplotlib library makes data visualization on multiple platforms using NumPy arrays and works with the entire SciPy stack. For charting 2D arrays and vectors, the Matplotlib marker module in Python is an information visualization resource that works across various platforms. Matplotlib would be created to work through the whole SciPy stack.

The Python Matplotlib markers component holds all of the approaches required to work with markers. The marker feature is used in both the plot and the scatter. In Matplotlib graphs, a Matplotlib Marker is a particular means of managing markers. Marker functions can be used to modify graphs that contain various sorts of markers and other signaling icons. To define the marker, we would alternatively specify the shortened string annotation argument. Let’s look at all of the available markers and how to utilize them.

Add Matplotlib Marker

This module includes marker-handling functionality. Both plot and scatter’s indicator configuration will be using this. The parameter ‘marker’ can be used to illustrate every location with such a specific sign. We will make a ‘star’ marker on a line graph in the subsequent example.

At the start of the program, we import Matplotlib along with another module known as NumPy modules. The matplotlib.pyplot library is responsible for the graphical functions and plotting methods and the NumPy library is utilized to handle different numeric values.

Further, we declare an array by using the built-in function of the NumPy library and here, we pass some random values as the parameters of this function. Now, we use the plot() function to draw the graph. This function has two arguments. We can specify the marker here. The ‘marker’ parameter is utilized to highlight distinct parts of the plot. Every single point in the graph is indicated with a ‘+’ marker.

In addition to this, we call the method to show the graph. The expected result for above explained code is attached here.

A ‘+’ plus marker has been used to mark every point as shown in the graph.

Insert Edge Color to Matplotlib Marker

To change the color of the identifiers’ boundaries, we just use the argument ‘markeredgecolor’ or the shortcut ‘mec’ to specify the hue to the edge of the marker. Here, we also adjust the dimensions of the indicators. We could use the argument ‘markersize’ or the abbreviated version, ‘ms’.

Here, we introduced matplotlib.pyplot and NumPy libraries that are utilized to create graphs and operate some numeric functions. We initialize the array by using the function of the NumPy library. Further, we utilize the plot() function.

We specified the symbol of the marker, the marker size, and the color of the marker edge. We have defined ‘d’ to the parameter ‘marker. It indicates that the marker should be diamond in shape. In the end, we employ the function to show the graph. The expected result for above explained code is attached here.

The edge color of the diamond is changed to yellow in this graph by using the parameter ‘markeredgecolor’. Similarly, the marker’s size is adjusted to 15 utilizing the parameter ‘markersize’.

Adjust the Matplotlib Marker Color

To modify the color of the symbols, we will use the argument ‘markerfacecolor’ or its shortened form ‘mfc’. Here, the value of mfc is ‘y’.

Before starting the code, we must include required libraries including matplotlib.pyplot as plt and NumPy for visual representations and operating some mathematical functions. Further, we create an array that contains random values. Now, we employ the plot() method. This function accepts different parameters to specify the symbol for the marker, its edge color, its shade, and its size. The argument ‘marker’ is set to ‘d’ which stands for ‘diamond’ symbol.

The diamond marker is set to yellow in this case. The marker size is set to ‘12’. The ‘markeredgecolor’ parameter is abbreviated as ‘mec’ here. Similarly, the ‘markerfacecolor’ is abbreviated as ‘mfc’. To color the marker, both ‘mec’ and ‘mfc’ are fixed to yellow in this example. For displaying the graph we apply the function. The expected result for above explained code is attached here.

Matplotlib Scatter Plot Contains Empty Circle Marker

In this illustration, the empty circle markers are being utilized to draw the graph. The blank circle indicator has no filling style.

First of all, we integrate matplotlib.pyplot as plt and NumPy libraries. Then we utilize the built-in function randn() of the NumPy library to set the values of axes. Here, we want to draw a scatter graph so we utilize the scatter() function. We pass different parameters to this function.

To acquire the empty circle markers, the argument ‘edgecolor’ is set to blue here. Further, function is called to represent the figure. The expected result for above explained code is attached here.

Customize the Linestyle of Matplotlib Marker

To customize the appearance of the displayed line, utilize the parameter linestyle or the relatively short ls.

We acquired Matplotlib and NumPy libraries. Matplotlib is a graphical framework for two-dimensional array layouts. It is a NumPy-based multi-platform used for visual analytics. Here we can modify the matplotlib marker.

In addition to this, we called the built-in function of the NumPy library to initialize the array for storing the values. Next, we utilized the plot() method to draw the figure. The parameter ’d-.g’ is being used to plot green-colored diamond-shaped markers with dashed lines in the illustration. We draw a dashed line in the graph by using ‘-.’. After all this, we display the figure. The expected result for above explained code is attached here.


In this article, we discussed Matplotlib markers in depth. In Matplotlib, we may create multiple segments through the use of the plt.plot() method to add the x- and y-axis coordinates for every alignment. Different mark points have been used in Matplotlib for drawing line and scatter plots. We have seen how to adjust the size, color, and form of markers.

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Kalsoom Bibi

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