Matplotlib Animation – A Helpful Illustrated Guide

Creating animations in matplotlib is reasonably straightforward. However, it can be tricky when starting, and there is no consensus for the best way to create them. In this article, I show you a few methods you can use to make amazing animations in matplotlib.

Matplotlib Animation Example

The hardest thing about creating animations in matplotlib is coming up with the idea for them. This article covers the basic ideas for line plots, and I may cover other plots such as scatter and 3D plots in the future. Once you understand these overarching principles, you can animate other plots effortlessly.

There are two classes

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Matplotlib 3D Plot Advanced

If you’ve already learned how to make basic 3d plots in maptlotlib and want to take them to the next level, then look no further. In this article, I’ll teach you how to create the two most common 3D plots (surface and wireframe plots) and a step-by-step method you can use to create any shape you can imagine.

In addition to import matplotlib.pyplot as plt and calling plt.show(), to create a 3D plot in matplotlib, you need to:

Import the Axes3D objectInitialize your Figure and Axes3D objectGet some 3D dataPlot it using Axes notation

Here’s a wireframe plot:

# Standard import
import matplotlib.pyplot as

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Matplotlib 3D Plot [Tutorial]

Are you tired with the same old 2D plots? Do you want to take your plots to the next level? Well look no further, it’s time to learn how to make 3D plots in matplotlib.

In addition to import matplotlib.pyplot as plt and calling plt.show(), to create a 3D plot in matplotlib, you need to:

Import the Axes3D objectInitialize your Figure and Axes3D objectsGet some 3D dataPlot it using Axes notation and standard function calls

# Standard import
import matplotlib.pyplot as plt

# Import 3D Axes
from mpl_toolkits.mplot3d import axes3d

# Set up Figure and 3D Axes
fig = plt.figure()
ax = fig.add_subplot(111, projection=’3d’)

# Get some

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Python Lists filter() vs List Comprehension – Which is Faster?

[Spoiler] Which function filters a list faster: filter() vs list comprehension? For large lists with one million elements, filtering lists with list comprehension is 40% faster than the built-in filter() method.

To answer this question, I’ve written a short script that tests the runtime performance of filtering large lists of increasing sizes using the filter() and the list comprehension methods.

My thesis is that the list comprehension method should be slightly faster for larger list sizes because it leverages the efficient cPython implementation of list comprehension and doesn’t need to call an extra function.

Related Article:

How to Filter a List in Python?

I

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Matplotlib 3D Plot – A Helpful Illustrated Guide

Are you tired with the same old 2D plots? Do you want to take your plots to the next level? Well look no further, it’s time to learn how to make 3D plots in matplotlib.

In addition to import matplotlib.pyplot as plt and calling plt.show(), to create a 3D plot in matplotlib, you need to:

Import the Axes3D objectInitialize your Figure and Axes3D objectsGet some 3D dataPlot it using Axes notation and standard function calls

# Standard import
import matplotlib.pyplot as plt

# Import 3D Axes
from mpl_toolkits.mplot3d import axes3d

# Set up Figure and 3D Axes
fig = plt.figure()
ax = fig.add_subplot(111, projection=’3d’)

# Get some 3D

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Matplotlib Legend – A Helpful Illustrated Guide

You’ve plotted some data in Matplotlib but you don’t know which data shows what? It’s time for a legend!

How to add a legend in Python’s Matplotlib library?

Label it with the label keyword argument in your plot method. Before plt.show(), call plt.legend() your plot will be displayed with a legend.

Here’s the minimal example:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [1, 4, 9], label=’squares’)
plt.plot([1, 2, 3], [1, 8, 27], label=’cubes’)

plt.legend()
plt.show()

In the following video, I’ll lead you through the article, step by step.

Prettier Example

# Import necessary modules
import matplotlib.pyplot as plt
import numpy as np

# Optional: Use seaborn style as it looks nicer than

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