In this tutorial we will show how to you can easily plot a function with Python and specifically using the Numpy, Matplotlib and Seaborn libraries.
Draw a continuous function graph with Python and Matplotlib
In this example we’ll going to go ahead and plot a function of two variables with Matplotlib. As an example, we’ll draw a simple line graph.
# import libraries import matplotlib.pyplot as plt import numpy as np plt.style.use('default') # creating the test data x = np.linspace(10, 100,10) y=np.power(x,3) # rendering the chart fig,ax= plt.subplots() plt.style.use('ggplot') ax.plot(x,y); ax.set_title('My Chart') ax.set_xlabel('X Axis') ax.set_ylabel('Y Axis');
- We first import the Numpy and Matplotlib libraries.
- We’ll then use numpy functions numpy.linspace and np.power to quickly generate the line plot underlying data.
- Then, we use Matplotlib library to create a figure and a single plot area (referred in Matplotlib as axes), then render the graph itself using the plt.plot command.
- Las we customize our chart and assign a title and labels to the x and y axes.
Note that there are hundreds of possible customization that you can apply to our chart, whcih we covered in our other visualization tutorials.
Note II: In the same fashion you can draw other defined functions either piecewise and continuous (such as sine (using np.sin() , linear and so forth).
Plotting a continuous function plot with Seaborn
# using seaborn import seaborn as sns import numpy as np # create data x = np.linspace(10, 100,10) y=np.power(x,3) # draw the graph sns.set_style('dark') fig,ax= plt.subplots() ax = sns.lineplot(x=x, y=y) ax.set_title('My Chart (using Seaborn)') ax.set_xlabel('X Axis') ax.set_ylabel('Y Axis');
The code is quite similar to the one provided above. The main difference is the usage of the Seaborn library.
- Import Seaborn and Numpy.
- Definition of the dataset and continuous function X^3.
- Rendering the graph using the Seaborn sns.lineplot method.
- Some simple customizations to the chart (title and labels).