How to Make an Animated Gif Fit for /r/dataisbeautiful

A good visualization should capture the interest of the audience and make an impression. Few things capture interest more than bright colors and movement. In this post, I’m going to show you exactly how to make an animated gif, so that you can go farm some internet points on /r/dataisbeautiful, maybe.

Here’s what we’re going to make:

Step 0 — Data for making an animated gif

Before you make a graph, you’ve gotta get your hands on some data. I grabbed some business data from StatsCanada available here. The data isn’t in the best shape, so here’s a pinch of pandas to make it suck less:

import pandas as pd
df = pd.read_csv("3310027001-noSymbol.csv", skiprows=7).iloc[1:5]
df = df.rename(columns={"Business dynamics measure": "status"})
df['status'] = df['status'].apply(lambda x: x[:-13])
df = pd.melt(df, id_vars="status", var_name="date", value_name="count")
df["date"] = pd.to_datetime(df["date"], format="%B %Y")
df['count'] = df['count'].apply(lambda x: int(x.replace(",", "")))
print(df.head())
#        status       date   count
# 0      Active 2015-01-01  775497
# 1     Opening 2015-01-01   40213
# 2  Continuing 2015-01-01  731116
# 3     Closing 2015-01-01   30979
# 4      Active 2015-02-01  778554

Step 1 — Graph

If you want to make an animated gif, you first have to make a single frame. Which, coincidentally, is just a graph:

from matplotlib import pyplot as plt
da = df[df["status"] == "Active"]
plt.plot(da["date"], da["count"])

Step 2 — Size

The graph could be bigger, and the y-axis limits could be adjusted. No problem, that’s just two extra lines of code: some code to size and limit:

plt.figure(figsize=(8, 5), dpi=300)
plt.plot(da["date"], da["count"])
plt.ylim([0, da['count'].max() * 1.1])

Step 3 — Tick

I like to manually set the ticks on my graphs, you don’t have to, but if you want to:

ymax = int(da['count'].max() * 1.1 // 1)
plt.figure(figsize=(8, 5), dpi=300)
plt.plot(da["date"], da["count"])
plt.ylim([0, ymax])
plt.yticks(range(0, ymax, 200_000))

Step 4 — Label

If someone saw our graph right now, without any context, they’d have no idea what’s going on. Let’s fix that by adding some labels:

plt.figure(figsize=(8, 5), dpi=300)
plt.plot(da["date"], da["count"])
plt.ylim([0, ymax])
plt.yticks(range(0, ymax, 200_000))
plt.title("Active Businesses in Canada (Seasonally Adjusted)")
plt.xlabel("Year")
plt.ylabel("Count")

Step 4.5 — Detour

Our graph is exclusively about “Active” businesses in Canada. Here’s what the “Opening” and “Closing” numbers look like:

dc = df[df["status"] == "Closing"]
do = df[df["status"] == "Opening"]
plt.plot(dc["date"], dc["count"], color='red', label="closing")
plt.plot(do["date"], do["count"], color='green', label="opening")
plt.legend()

Step 5 — Combine to make an animated gif

The “Opening and Closing” graph adds some interesting color to the “Active” data. Let’s combine both with some fancy-pants matplotlib:

rows = 7
figure = plt.figure(figsize=(8, 4), constrained_layout=False, dpi=300)
grid = plt.GridSpec(
    nrows=rows,
    ncols=1,
    wspace=0,
    hspace=0.5,
    figure=figure
)
main = plt.subplot(grid[:5, 0])
sub = plt.subplot(grid[5:, 0])
main.plot(da["date"], da["count"])
sub.plot(do["date"], do["count"])
sub.plot(dc["date"], dc["count"])

Step 6 — Colour

I’m not keen on the colors or spacing of what we have right now. To fix, along with some axis adjustments, here’s what you’ll need:

figure = plt.figure(figsize=(8, 4), constrained_layout=False, dpi=300)
grid = plt.GridSpec(
    nrows=rows,
    ncols=1,
    wspace=0,
    hspace=0.75,
    figure=figure
)
main = plt.subplot(grid[:5, 0])
sub = plt.subplot(grid[5:, 0])
main.plot(da["date"], da["count"], color="purple")
sub.plot(do["date"], do["count"], color="blue")
sub.plot(dc["date"], dc["count"], color="red")
main.set_xticks([])
main.set_ylim([0, ymax])
main.set_yticks(range(0, ymax, 200_000))
main.set_yticklabels([0, "200K", "400K", "600K", "800K\nbusinesses"])
sub.set_ylim([0, 110_000])
sub.set_yticks([0, 100_000])
sub.set_yticklabels([0, "100K"])

Step 7 — Refactor

Our graph code is nearly ready to go. We just need to refactor it so that we can take an individual date and build an individual frame for that date. I’ve also added some vlines and fixed the xlims to improve legibility and ensure that the plotting space is consistent across plots:

date = pd.Timestamp("2019-08-01")
xmin = df['date'].min()
xmax = df['date'].max()
dd = df[df["date"] <= date]
dc = dd[dd["status"] == "Closing"]
do = dd[dd["status"] == "Opening"]
da = dd[dd["status"] == "Active"]
figure = plt.figure(figsize=(8, 4), constrained_layout=False, dpi=300)
grid = plt.GridSpec(
    nrows=rows,
    ncols=1,
    wspace=0,
    hspace=1.25,
    figure=figure
)
main = plt.subplot(grid[:5, 0])
sub = plt.subplot(grid[5:, 0])
main.plot(da["date"], da["count"], color="#457b9d")
main.vlines(date, ymin=0, ymax=1e20, color="#000000")
sub.plot(do["date"], do["count"], color="#a8dadc")
sub.plot(dc["date"], dc["count"], color="#e63946")
sub.vlines(date, ymin=0, ymax=1e20, color="#000000")
main.set_xlim([xmin, xmax])
main.set_xticks([])
main.set_ylim([0, ymax])
main.set_yticks(range(0, ymax, 200_000))
main.set_yticklabels([0, "200K", "400K", "600K", "800K"])
main.set_title("Active Businesses in Canada")
sub.set_xlim([xmin, xmax])
sub.set_xticks([date])
sub.set_xticklabels([date.strftime("%B '%y")])
sub.set_ylim([0, 110_000])
sub.set_yticks([0, 100_000])
sub.set_yticklabels([0, "100K"])
sub.set_title("Businesses Opening and Closing")

Step 7.5 — Functionize to make an animated gif

In order to build a bunch of frames on a bunch of dates, we should wrap our code in a function:

def plot(date):
    dd = df[df["date"] <= date]
    dc = dd[dd["status"] == "Closing"]
    do = dd[dd["status"] == "Opening"]
    da = dd[dd["status"] == "Active"]
    figure = plt.figure(figsize=(8, 4), constrained_layout=False, dpi=300)
    grid = plt.GridSpec(
        nrows=rows,
        ncols=1,
        wspace=0,
        hspace=1.25,
        figure=figure
    )
    main = plt.subplot(grid[:5, 0])
    sub = plt.subplot(grid[5:, 0])
    main.plot(da["date"], da["count"], color="#457b9d")
    main.vlines(date, ymin=0, ymax=1e20, color="#000000")
    sub.plot(do["date"], do["count"], color="#a8dadc")
    sub.plot(dc["date"], dc["count"], color="#e63946")
    sub.vlines(date, ymin=0, ymax=1e20, color="#000000")
    main.set_xlim([xmin, xmax])
    main.set_xticks([])
    main.set_ylim([0, ymax])
    main.set_yticks(range(0, ymax, 200_000))
    main.set_yticklabels([0, "200K", "400K", "600K", "800K"])
    main.set_title("Active Businesses in Canada")
    sub.set_xlim([xmin, xmax])
    sub.set_xticks([date])
    sub.set_xticklabels([date.strftime("%b '%y")])
    sub.set_ylim([0, 110_000])
    sub.set_yticks([0, 100_000])
    sub.set_yticklabels([0, "100K"])
    sub.set_title("Businesses Opening and Closing");

So that we can build a frame with just one call:

plot(pd.Timestamp("2017-06-01"))

Step 8 — import gif

To turn static frames into an animated gif, all we have to do now is to install and import the gif package:

import gif

Decorate the plot function with gif.frame:

@gif.frame
def plot(date):
    dd = df[df["date"] <= date]
    dc = dd[dd["status"] == "Closing"]
    do = dd[dd["status"] == "Opening"]
    da = dd[dd["status"] == "Active"]
    figure = plt.figure(figsize=(8, 4), constrained_layout=False, dpi=300)
    grid = plt.GridSpec(
        nrows=7,
        ncols=1,
        wspace=0,
        hspace=1.25,
        figure=figure
    )
    main = plt.subplot(grid[:5, 0])
    sub = plt.subplot(grid[5:, 0])
    main.plot(da["date"], da["count"], color="#457b9d")
    main.vlines(date, ymin=0, ymax=1e20, color="#000000")
    sub.plot(do["date"], do["count"], color="#a8dadc")
    sub.plot(dc["date"], dc["count"], color="#e63946")
    sub.vlines(date, ymin=0, ymax=1e20, color="#000000")
    main.set_xlim([xmin, xmax])
    main.set_xticks([])
    main.set_ylim([0, ymax])
    main.set_yticks(range(0, ymax, 200_000))
    main.set_yticklabels([0, "200K", "400K", "600K", "800K"])
    main.set_title("Active Businesses in Canada")
    sub.set_xlim([xmin, xmax])
    sub.set_xticks([date])
    sub.set_xticklabels([date.strftime("%b '%y")])
    sub.set_ylim([0, 110_000])
    sub.set_yticks([0, 100_000])
    sub.set_yticklabels([0, "100K"])
    sub.set_title("Businesses Opening and Closing");

Build all the frames:

dates = pd.date_range(df['date'].min(), df['date'].max(), freq="1MS")
frames = [plot(date) for date in dates]

And save the animation to disk:

gif.save(frames, "businesses.gif", duration=5, unit="s", between="startend")

Now it’s your turn to find some interesting data and turn it into a gif.

And if you want to learn more, I’m running a workshop on gifs with ODSC/AI+ on October 22, called “Animating Data: Turn any Matplotlib plot into an animated gif.” Hope to see you in class!

More on the course on how to make an animated gif:

In this course, Max will help you turn any Matplotlib plot into an animated visualization with just a couple of extra lines of code. Whereas animation used to be something that required fancy tooling, Max will show you how to use simple Python decorators to stitch together multiple plots like frames in a movie. By the end of this session, you’ll be able to capture interest and make an impression with any presentation or spot of data analysis!