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Learning about the metrics that drive successful products

Animating Pandas Plots

Using the Celluloid module to animate pandas plots


2 minutes read

One of the powerful aspects of using Python as a Product Manager is that you have a lot of control over the presentation of your data analysis. And, once you’ve created your amazing plot, you can quickly and easily update the underlying data to provide updates. One of the lesser known aspects of plotting is the ability to create an animation, showing how particular trend changes over time or to incrementally build a complex plot. In this post, we’ll look at a simple method for creating an animated plot from a Pandas DataFrame using the Celluloid module.

Installing Celluloid

Celluloid is a module which wraps some underlying matplotlib functions to create an easy to use animation library for a plot.

Installing it is easy:

pip install celluloid

Now, we should all be ready to go!

Animating a plot

This example builds on the monte carlo business case forecast we worked on in the last post. In this example, we have a dataframe which has columns for one hundred simulations of a business case. We can use celluloid to visualise these being run one at a time, finally adding some percentile outcomes for clarity.

So, in the code below we will loop through each column (stored in the array forecast_licences_cols), and plot an increasing number of the simulated runs:

from matplotlib import pyplot as plt
from celluloid import Camera

fig= plt.figure()
ax = plt.gca()
camera = Camera(fig)

for i in range(1,99):
    mc.df[mc.forecast_licences_cols[:i]].plot(legend=False, color='#444444',linewidth=0.25, alpha=0.33, ax=ax)

mc.df[mc.forecast_licences_cols[:100]].plot(legend=False, color='#444444',linewidth=0.25, alpha=0.33, ax=ax)
mc.df['forecast_licences_0.5_quantile'].plot(ax=ax, linewidth=2)
mc.df['forecast_licences_0.9_quantile'].plot(ax=ax, linewidth=2)
mc.df['forecast_licences_0.1_quantile'].plot(ax=ax, linewidth=2)


animation = camera.animate()

Each time we call camera.snap() a frame is taken for our final movie. We save the movie at the end once we’ve iterated through each column.

Viewing celluloid animations in a Jupyter Notebook

If you’re running the code in a Jupyter Notebook then there is a special helper method which will allow for inline viewing of your amazing animation. It makes use of iPythons HTML display capabilities, so, you’ll need to from IPython.display import HTML

Then, at the end of your animation (after saving the file) simply add the following:


And then you’ll see your video inline!

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Hey, I'm Joshua, a Product Manager and data fan. This is my blog and a place to post random musings and tutorials.