Matplotlib.pyplot.scatter() in Python
There are various ways of creating plots using matplotlib.pyplot.scatter() in Python, There are some examples that illustrate the matplotlib. pyplot.scatter() function in matplotlib.plot:
- Basic Scatter Plot
- Scatter Plot With Multiple Datasets
- Bubble Chart Plot
- Customized Scatter Plot
Scatter Plot in Matplotlib
By importing matpltlib. plot () we created a scatter plot. It defines x and y coordinates, then plots the points in blue and displays the plot.
Python3
import matplotlib.pyplot as plt x = [ 5 , 7 , 8 , 7 , 2 , 17 , 2 , 9 , 4 , 11 , 12 , 9 , 6 ] y = [ 99 , 86 , 87 , 88 , 100 , 86 , 103 , 87 , 94 , 78 , 77 , 85 , 86 ] plt.scatter(x, y, c = "blue" ) # To show the plot plt.show() |
Output :
Plot Multiple Datasets on a Scatterplot
The below code generates a scatter plot showcasing two distinct datasets, each with its set of x and y coordinates. The code employs different markers, colors, and styling options for enhanced visualization.
Python3
import matplotlib.pyplot as plt # dataset-1 x1 = [ 89 , 43 , 36 , 36 , 95 , 10 , 66 , 34 , 38 , 20 ] y1 = [ 21 , 46 , 3 , 35 , 67 , 95 , 53 , 72 , 58 , 10 ] # dataset2 x2 = [ 26 , 29 , 48 , 64 , 6 , 5 , 36 , 66 , 72 , 40 ] y2 = [ 26 , 34 , 90 , 33 , 38 , 20 , 56 , 2 , 47 , 15 ] plt.scatter(x1, y1, c = "pink" , linewidths = 2 , marker = "s" , edgecolor = "green" , s = 50 ) plt.scatter(x2, y2, c = "yellow" , linewidths = 2 , marker = "^" , edgecolor = "red" , s = 200 ) plt.xlabel( "X-axis" ) plt.ylabel( "Y-axis" ) plt.show() |
Output :
Bubble Plots in Matplotlib
This code generates a bubble chart using Matplotlib. It plots points with specified x and y coordinates, each represented by a bubble with a size determined by the bubble_sizes
list. The chart has customization for transparency, edge color, and linewidth. Finally, it displays the plot with a title and axis labels.
Python3
import matplotlib.pyplot as plt # Data x_values = [ 1 , 2 , 3 , 4 , 5 ] y_values = [ 2 , 3 , 5 , 7 , 11 ] bubble_sizes = [ 30 , 80 , 150 , 200 , 300 ] # Create a bubble chart with customization plt.scatter(x_values, y_values, s = bubble_sizes, alpha = 0.6 , edgecolors = 'b' , linewidths = 2 ) # Add title and axis labels plt.title( "Bubble Chart with Transparency" ) plt.xlabel( "X-axis" ) plt.ylabel( "Y-axis" ) # Display the plot plt.show() |
Output :
Custom a Matplotlib Scatterplot
By importing Matplotlib we create a customized scatter plot using Matplotlib and NumPy. It generates random data for x and y coordinates, colors, and sizes. The scatter plot is then created with customized properties such as color, size, transparency, and colormap. The plot includes a title, axis labels, and a color intensity scale. Finally, the plot is displayed
Python3
import matplotlib.pyplot as plt import numpy as np # Generate random data x = np.random.rand( 50 ) y = np.random.rand( 50 ) colors = np.random.rand( 50 ) sizes = 100 * np.random.rand( 50 ) # Create a customized scatter plot plt.scatter(x, y, c = colors, s = sizes, alpha = 0.7 , cmap = 'viridis' ) # Add title and axis labels plt.title( "Customized Scatter Plot" ) plt.xlabel( "X-axis" ) plt.ylabel( "Y-axis" ) # Display color intensity scale plt.colorbar(label = 'Color Intensity' ) # Show the plot plt.show() |
Output :
Conclusion
In conclusion, matplotlib.pyplot.scatter()
Python is a versatile and powerful tool for visualizing relationships between variables through scatter plots. Its flexibility allows for the customization of markers, colors, sizes, and other properties, providing a dynamic means of representing complex data patterns. Whether for basic exploratory analysis or detailed data interpretation, this function plays a crucial role in creating informative and visually appealing scatter plots within the Python programming environment.
matplotlib.pyplot.scatter() in Python
Matplotlib stands as an extensive library in Python, offering the capability to generate static, animated, and interactive visualizations. The Matplotlib.pyplot.scatter() in Python extends to creating diverse plots such as scatter plots, bar charts, pie charts, line plots, histograms, 3-D plots, and more.
For a more in-depth understanding, additional information can be found in the guide titled “Python Matplotlib – An Overview.”