Best Python Data Visualization Tools to Buy in November 2025

1

Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming

BUY & SAVE

$27.53 $49.99

Save 45%

2

Python Programming for Beginners: The Complete Python Coding Crash Course - Boost Your Growth with an Innovative Ultra-Fast Learning Framework and Exclusive Hands-On Interactive Exercises & Projects

BUY & SAVE

$22.53 $25.95

Save 13%

3

Learning Python: Powerful Object-Oriented Programming

BUY & SAVE

$55.68 $79.99

Save 30%

4

Python Programming for Beginners: The Complete Guide to Mastering Python in 7 Days with Hands-On Exercises – Top Secret Coding Tips to Get an Unfair Advantage and Land Your Dream Job!

BUY & SAVE

$24.99

5

Python Programming Language: a QuickStudy Laminated Reference Guide

BUY & SAVE

$8.95

6

Fluent Python: Clear, Concise, and Effective Programming

BUY & SAVE

$43.99 $79.99

Save 45%

7

Python All-in-One For Dummies (For Dummies: Learning Made Easy)

BUY & SAVE

$24.77 $44.99

Save 45%

8

Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners

• LEARN PYTHON EASILY WITH A BEGINNER-FRIENDLY APPROACH!
• PREMIUM QUALITY BOOK ENSURES DURABILITY AND LONG-LASTING USE.
• AUTOMATE TEDIOUS TASKS AND BOOST PRODUCTIVITY WITH PRACTICAL SKILLS!

BUY & SAVE

$36.04 $49.99

Save 28%

9

Absolute Beginner's Guide to Python Programming: Master Coding Quickly with Hands-On, Real-World Projects, Step-By-Step Guidance, and Comprehensive Learning for All Ages (Absolute Beginner's Guides)

BUY & SAVE

$22.95

+

ONE MORE?

To add a plot to a figure in Matplotlib, you can follow these steps:

1. Import the necessary libraries:

import matplotlib.pyplot as plt import numpy as np

1. Create a figure and an axis:

fig, ax = plt.subplots()

1. Generate some data points to plot:

x = np.linspace(0, 10, 100) y = np.sin(x)

1. Add the plot to the axis:

ax.plot(x, y)

You can customize the plot by specifying various parameters inside the plot function, such as line style, color, and markers.

1. Add labels to the x-axis and y-axis:

ax.set_xlabel('X-axis') ax.set_ylabel('Y-axis')

1. Add a title to the figure:

ax.set_title('Plot Example')

1. Display the figure:

plt.show()

These steps demonstrate a basic way to add a plot to a figure in Matplotlib. By exploring the official Matplotlib documentation, you can find more advanced plotting techniques and customization options to enhance your plots further.

What are the different types of markers available in Matplotlib?

There are multiple types of markers available in Matplotlib. Some of the commonly used marker types include:

1. "." - Point marker (dot)
2. "," - Pixel marker
3. "o" - Circle marker
4. "v" - Triangle_down marker
5. "^" - Triangle_up marker
6. "<" - Triangle_left marker
7. ">" - Triangle_right marker
8. "1" - Tri_down marker
9. "2" - Tri_up marker
10. "3" - Tri_left marker
11. "4" - Tri_right marker
12. "8" - Octagon marker
13. "s" - Square marker
14. "p" - Pentagon marker
15. "P" - Plus (filled) marker
16. "*" - Star marker
17. "h" - Hexagon1 marker
18. "H" - Hexagon2 marker
19. "+" - Plus marker
20. "x" - X marker
21. "X" - X (filled) marker
22. "D" - Diamond marker
23. "d" - Thin diamond marker
24. "|" - Vline marker
25. "_" - Hline marker

These markers can be selected and customized while plotting data points on a graph using Matplotlib.

How to create a scatter plot matrix in Matplotlib?

To create a scatter plot matrix in Matplotlib, you can use the scatter_matrix() function from the pandas.plotting module. Here is an example code that demonstrates how to create a scatter plot matrix:

import pandas as pd import matplotlib.pyplot as plt from pandas.plotting import scatter_matrix

Create a DataFrame with some sample data

data = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12], 'D': [13, 14, 15, 16]})

Create a scatter plot matrix

scatter_matrix(data, alpha=0.8, figsize=(6, 6), diagonal='kde')

Show the plot

plt.show()

In the code above, we first create a DataFrame data with some sample data. Then we use scatter_matrix(data) to create a scatter plot matrix. The alpha parameter controls the transparency of the markers, the figsize parameter sets the size of the plot, and the diagonal parameter specifies what kind of plots to show along the diagonal. In this example, we use 'kde' to show kernel density estimation plots. Finally, we call plt.show() to display the plot.

How to change the line style in a Matplotlib plot?

To change the line style in a Matplotlib plot, you can use the linestyle parameter in the plot() function. Here are the steps:

1. Import the required libraries:

import matplotlib.pyplot as plt

1. Create some data for the x and y axes:

x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10]

1. Specify the line style using the linestyle parameter:

plt.plot(x, y, linestyle='--')

Here, linestyle='--' sets the line style to dashed. You can change it to other styles like '-' for solid, ':' for dotted, or '-.' for dash-dot. 4. Display the plot using plt.show():

plt.show()

The complete code would look like this:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10]

plt.plot(x, y, linestyle='--') plt.show()

Running this code will display a line plot with a dashed line style.

How to add markers to a line graph in Matplotlib?

To add markers to a line graph in Matplotlib, you can use the marker parameter of the plot() function. Here is an example code that demonstrates this:

import matplotlib.pyplot as plt

Data

x = [1, 2, 3, 4, 5] y = [1, 3, 2, 4, 5]

Plot the line graph with markers

plt.plot(x, y, marker='o')

Add labels and title

plt.xlabel('x-axis') plt.ylabel('y-axis') plt.title('Line graph with markers')

Show the graph

plt.show()

In this example, marker='o' adds circular markers to the data points. You can use different marker styles, such as 'o' for circles, 's' for squares, or 'x' for crosses. You can also specify the size and color of the markers using additional parameters like markersize and markerfacecolor.

Note that you need to have Matplotlib installed in order to run this code.

What is Matplotlib used for?

Matplotlib is a popular data visualization library used in Python. It is used for creating various types of plots and charts, including line plots, scatter plots, bar plots, histograms, pie charts, and more. It provides a flexible and user-friendly interface to visualize data and display it in a visually appealing and informative manner. Matplotlib is widely used in scientific research, data analysis, and machine learning to explore and understand patterns, trends, and relationships in data.

How to set x-axis and y-axis labels in Matplotlib?

To set the x-axis and y-axis labels in Matplotlib, you can use the xlabel() and ylabel() functions respectively. Here is an example:

import matplotlib.pyplot as plt

Generate some example data

x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10]

Plot the data

plt.plot(x, y)

Set the x-axis label

plt.xlabel('X-axis')

Set the y-axis label

plt.ylabel('Y-axis')

Display the plot

plt.show()

In this example, the xlabel() function is used to set the x-axis label to 'X-axis', and the ylabel() function is used to set the y-axis label to 'Y-axis'.

You can also customize the labels by adding additional parameters to these functions. For example, you can specify the color, font size, and font weight of the labels.