Best Matplotlib Books to Buy in October 2025
Matplotlib 3.0 Cookbook: Over 150 recipes to create highly detailed interactive visualizations using Python
Python for Engineering and Scientific Computing: Practical Applications with NumPy, SciPy, Matplotlib, and More (Rheinwerk Computing)
Python GUI Programming with Tkinter: Design and build functional and user-friendly GUI applications, 2nd Edition
Matplotlib 2.x By Example: Multi-dimensional charts, graphs, and plots in Python
Python Data Cleaning Cookbook: Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
Effective Visualization: Exploiting Matplotlib & Pandas (Treading on Python)
Python Data Analytics: With Pandas, NumPy, and Matplotlib
50 Days of Data Analysis with Python: The Ultimate Challenges Book for Beginners.: Hands-on Challenges with pandas, NumPy, Matplotlib, Sklearn and Seaborn
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Data Visualization in Python with Pandas and Matplotlib
In Matplotlib, you can set axis values by using the set_xlim() and set_ylim() methods for the x-axis and y-axis respectively. You can pass in the minimum and maximum values for the axis range as arguments to these methods.
For example, to set the x-axis range from 0 to 10 and the y-axis range from 0 to 20, you can use the following code:
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [10, 15, 13, 18]) plt.xlim(0, 10) plt.ylim(0, 20)
plt.show()
This will set the x-axis values to range from 0 to 10 and the y-axis values to range from 0 to 20 in the plot. You can adjust the range of the axes to better fit your data and make the plot more informative.
What is the advantage of hiding axis labels in matplotlib?
Hiding axis labels in a matplotlib plot can sometimes be advantageous in situations where the labels may be distracting or unnecessary. By removing axis labels, the overall visual presentation of the plot can be cleaner and more focused on the data being presented. This can be particularly useful when creating plots for presentations or publications where simplicity and clarity are important. Additionally, hiding axis labels can also help to reduce clutter in plots with multiple subplots or complicated layouts, making it easier for viewers to interpret the data.
How to add a secondary y-axis in matplotlib?
To add a secondary y-axis in Matplotlib, you can create a second set of axes and set the right y-axis to be the secondary one. Here is an example code snippet demonstrating how to do this:
import matplotlib.pyplot as plt
create some data
x = range(10) y1 = [i**2 for i in x] y2 = [i**3 for i in x]
fig, ax1 = plt.subplots()
plot the first set of data
ax1.plot(x, y1, 'b') ax1.set_ylabel('Primary Y-axis', color='b')
create a second set of axes
ax2 = ax1.twinx() ax2.plot(x, y2, 'r') ax2.set_ylabel('Secondary Y-axis', color='r')
plt.show()
In this code, we first create a set of data for two different y-values. We then create the first set of axes using plt.subplots(). We plot the first data set on this axis and set the label for the primary y-axis.
Next, we create a second set of axes with ax1.twinx() which shares the same x-axis as the first set of axes. We plot the second data set on this axis and set the label for the secondary y-axis.
Finally, we display the plot with plt.show(). This will show a plot with both y-values plotted on separate y-axes.
How to display the grid lines only on one axis in matplotlib?
You can achieve this by setting the 'axis' parameter of the grid() function in matplotlib. To display grid lines only on one axis, you can set the 'axis' parameter to either 'x' or 'y' depending on which axis you want the grid lines to appear on.
Here is an example code snippet to display grid lines only on the x-axis:
import matplotlib.pyplot as plt
Create some data
x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10]
Plot the data
plt.plot(x, y)
Display grid lines only on the x-axis
plt.grid(axis='x')
Show the plot
plt.show()
Similarly, if you want to display grid lines only on the y-axis, you can set the 'axis' parameter to 'y':
import matplotlib.pyplot as plt
Create some data
x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10]
Plot the data
plt.plot(x, y)
Display grid lines only on the y-axis
plt.grid(axis='y')
Show the plot
plt.show()
You can experiment with different values for the 'axis' parameter to display grid lines on both axes or none at all.
How to set a baseline value for the x-axis in matplotlib?
To set a baseline value for the x-axis in Matplotlib, you can use the set_xlim() function to specify the range of values that will be displayed on the x-axis. Here's an example of how you can set a baseline value of 0 for the x-axis:
import matplotlib.pyplot as plt
Generate some data
x = [1, 2, 3, 4, 5] y = [10, 20, 15, 25, 30]
Create a plot
plt.plot(x, y)
Set the baseline value for the x-axis to 0
plt.xlim(0, max(x))
Display the plot
plt.show()
In this example, plt.xlim(0, max(x)) sets the x-axis limits from 0 to the maximum value in the data set. This effectively sets the baseline value for the x-axis to 0. You can adjust the values passed to plt.xlim() to suit your specific requirements.
How to change the tick marks on the y-axis in matplotlib?
To change the tick marks on the y-axis in matplotlib, you can use the yticks() function. Here is an example code snippet to demonstrate how to change the tick marks on the y-axis:
import matplotlib.pyplot as plt
Create some sample data
x = [1, 2, 3, 4, 5] y = [10, 20, 15, 25, 30]
Plot the data
plt.plot(x, y)
Customize the tick marks on the y-axis
plt.yticks([0, 10, 20, 30, 40])
Display the plot
plt.show()
In this example, the yticks() function is used to set the tick marks on the y-axis to [0, 10, 20, 30, 40]. You can customize the tick marks by passing a list or array of values to the yticks() function.
You can also use other customization options with the yticks() function, such as changing the tick labels by passing a list of labels as the second argument.
# Customize the tick marks and labels on the y-axis plt.yticks([0, 10, 20, 30, 40], ['zero', 'ten', 'twenty', 'thirty', 'forty'])
By using the yticks() function, you can easily change the tick marks on the y-axis in matplotlib to suit your specific needs.
How to set a logarithmic scale on the x-axis in matplotlib?
To set a logarithmic scale on the x-axis in matplotlib, you can use the set_xscale method of the Axes object. Here is an example code snippet to demonstrate this:
import matplotlib.pyplot as plt import numpy as np
Create some data
x = np.arange(1, 10) y = np.log(x)
Create a plot
plt.plot(x, y) plt.xlabel('X Axis') plt.ylabel('Y Axis')
Set the x-axis to a logarithmic scale
plt.xscale('log')
Show the plot
plt.show()
In this code snippet, we first create some sample data x and y. We then plot the data using plt.plot() and set the x-axis to a logarithmic scale using plt.xscale('log'). Finally, we display the plot using plt.show().