![]() ![]() In this example, we use the marker parameter to set the marker style to a star ( '*') and the s parameter to set the marker size to 100. Plt.scatter(x, y, marker='*', s=100, edgecolors='black', facecolors='red') # Create the scatter plot with customized marker style and size Here’s an example: import matplotlib.pyplot as plt To customize the marker style and size in a scatter plot, you can use the marker and s parameters in the scatter() function in Matplotlib. How to Customize the Marker Style and Size in Scatter Plot The x-axis will be labeled “X-axis”, the y-axis will be labeled “Y-axis”, and the title of the plot will be “Simple Scatter Plot”. This will create a scatter plot with the data points (1,3), (2,5), (3,4), (4,6), and (5,8) plotted on the x-y plane. Here’s the full code: import matplotlib.pyplot as plt Display the plot using the show() function:.Add axis labels and a title to the plot:.Create the scatter plot using the scatter() function:.Create two arrays with data for the x and y variables:.To create a simple scatter plot in Matplotlib, you can follow these steps: In addition, scatter plots can be used to visualize data in a way that is easy to interpret and communicate to others. Scatter plots can also be used to identify the strength and direction of the relationship between the two variables. They are particularly useful for identifying patterns, trends, and potential outliers in the data. Scatter plots are used to visualize the relationship between two variables. How to Add a Regression Line to Scatter Plots. ![]()
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