Best Python Graphing Tools to Buy in October 2025

1

Texas Instruments TI-84 Plus CE Color Graphing Calculator, Black

• HIGH-RES LED DISPLAY WITH SPLIT-SCREEN FOR ENHANCED VIEWING.
• RECHARGEABLE BATTERY AND PRELOADED APPS FOR ENDLESS FUN!
• FOURTEEN ZOOM FEATURES AND SEVEN GRAPH STYLES FOR VERSATILITY.

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$110.00 $150.00

Save 27%

2

TI-84 Plus CE Python Enhanced Graphing plus Software, Iris/Purple

• STUNNING FULL-COLOR DISPLAY FOR VIBRANT, EYE-CATCHING VISUALS.
• LONG-LASTING RECHARGEABLE BATTERY FOR ALL-DAY USE.
• SLEEK DESIGN THAT COMPLEMENTS ANY MODERN SETTING.

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$120.00 $150.00

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3

Python for the TI-84: Powerful Python programs and games for the TI-84 Plus CE Graphing Calculator (Python Programming for Calculators - Practical and ... for students and adults alike! Book 2)

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$9.99

4

TI-84 Plus CE Graphing Calculator, Fractal Frost

• LIGHTWEIGHT & DURABLE FOR YEARS OF CLASSROOM USE.
• VIBRANT FULL-COLOR BACKLIT DISPLAY FOR ENGAGING LEARNING.
• LONG-LASTING BATTERY: UP TO 1 MONTH ON A SINGLE CHARGE.

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$111.00 $150.00

Save 26%

5

Python on the Numworks Graphing Calculator

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$4.95

6

Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools

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$26.81 $43.99

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7

Casio fx-9750GIII Graphing Calculator | Natural Textbook Display | Python Programming | Ideal for Exams, STEM & Advanced Math | Black

• NATURAL TEXTBOOK DISPLAY: SIMPLIFIES COMPLEX MATH AT A GLANCE!
• STUDENT-FRIENDLY & VERSATILE: PERFECT FOR ALL MATH LEVELS, FROM PRE-ALGEBRA TO AP.
• EXAM-APPROVED & USB READY: TRUSTED FOR TESTS; EASILY TRANSFER FILES!

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$49.99

8

Casio fx-CG100 ClassWiz® Color Graphing Calculator with 3D Graph & Python | Large High-Res Display, Basic & Advanced Functions | Ideal for Exams, STEM, Programming & Advanced

• VIVID 3D GRAPHING & DISPLAY: EXPERIENCE CLEAR VISUALS WITH 65,000 COLORS.

• INTUITIVE MATH FUNCTIONS: EASY NAVIGATION FOR COMPLEX CALCULATIONS AND GRAPHING.

• EXAM-READY FEATURES: BUILT-IN EXAM MODE FOR STANDARDIZED TESTING APPROVAL.

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$139.99

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ONE MORE?

To plot a 3D graph in Python using Matplotlib, you first need to import the necessary libraries:

import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D

Next, create a figure and an axis object:

fig = plt.figure() ax = fig.add_subplot(111, projection='3d')

Now, you can plot your 3D graph by specifying the x, y, and z coordinates:

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

ax.scatter(x, y, z)

Finally, you can customize your plot by adding labels, titles, and legends:

ax.set_xlabel('X Label') ax.set_ylabel('Y Label') ax.set_zlabel('Z Label')

plt.title('3D Scatter Plot') plt.legend(['Data points'])

plt.show()

How to install matplotlib in Python?

To install matplotlib in Python, you can use pip, which is the default package manager for Python.

Here are the steps to install matplotlib using pip:

1. Open the command prompt or terminal on your computer.
2. Run the following command to install matplotlib:

pip install matplotlib

1. Wait for the installation process to complete. Once it is done, you will have matplotlib installed on your Python environment.

You can now import matplotlib in your Python scripts and use it to create visualizations like plots and charts.

How to add text annotations to a plot in matplotlib?

To add text annotations to a plot in matplotlib, you can use the plt.annotate() function. Here is an example:

import matplotlib.pyplot as plt

Create some data

x = [1, 2, 3, 4, 5] y = [2, 3, 5, 7, 11]

Create the plot

plt.plot(x, y)

Add text annotation at point (3, 5)

plt.annotate('This is point (3, 5)', xy=(3, 5), xytext=(4, 6), arrowprops=dict(facecolor='black', shrink=0.05))

plt.show()

In this example, the plt.annotate() function is used to add text annotation at the point (3, 5) on the plot. The xy argument specifies the coordinates of the point to annotate, and the xytext argument specifies the coordinates of the text annotation. The arrowprops argument allows you to customize the arrow properties connecting the annotated point and the text.

How to create a histogram in matplotlib?

You can create a histogram in Matplotlib by following these steps:

1. Import the necessary libraries:

import matplotlib.pyplot as plt import numpy as np

1. Generate some random data to plot:

data = np.random.normal(0, 1, 1000)

1. Create a histogram using the plt.hist() function:

plt.hist(data, bins=30, edgecolor='black') # bins parameter determines the number of bins in the histogram

1. Add labels and a title to the histogram:

plt.xlabel('Value') plt.ylabel('Frequency') plt.title('Histogram of Data')

1. Display the histogram:

plt.show()

By following these steps, you should be able to create a histogram using Matplotlib in Python.

What is the difference between plot and scatter in matplotlib?

In Matplotlib, both plot and scatter functions are used to create visualizations of data, but there are some differences between the two:

1. Plot:

• "plot" is used to create a line plot by connecting the data points with lines.
• It is particularly useful when you want to show the relationship between two continuous variables.
• The "plot" function is typically used for displaying time series data or data that has a natural ordering.
• It is also useful for showing trends or patterns in the data.

1. Scatter:

• "scatter" is used to create a scatter plot, where each data point is represented individually with a marker.
• It is useful when you want to visualize the relationship between two variables that are not inherently connected by a line.
• Scatter plots are particularly useful for identifying correlations or clusters in the data.
• The "scatter" function is often used when you want to see the distribution of data points or identify outliers.

In summary, while both plot and scatter functions can be used to create visualizations of data, the choice between the two depends on the nature of the data you are working with and the type of relationship you want to display.

How to save a plot as an image file in matplotlib?

In matplotlib, you can save a plot as an image file by using the savefig() function. Here's an example of how you can save a plot as a PNG image file:

import matplotlib.pyplot as plt

Create a plot

plt.plot([1, 2, 3, 4], [1, 4, 9, 16])

Save the plot as a PNG image file

plt.savefig('plot.png')

Display the plot

plt.show()

In this example, the savefig() function is used to save the plot as a PNG image file named plot.png in the current directory. You can also specify the file format using the format argument, such as 'jpg', 'pdf', 'svg', etc.

Additionally, you can specify other parameters in the savefig() function, such as the DPI (dots per inch) of the saved image, the quality of the image (for JPEG format), and whether or not to include the white space around the plot.