Plotting with Python

Why make graphs with code?

Graphs help people understand data quickly - much faster than looking at huge tables full of numbers. You’ve probably made a graph in MicroSoft Excel by clicking buttons and changing settings. That’s fine for one or two graphs, but:

What if you needed to make 10, 100, or even 1,000 graphs?
Doing it all by hand would take ages and could lead to mistakes!

Using Python, we can:

Write instructions to make the graph for us
Re-use the same code for lots of different data
Save time and make fewer errors
Create really professional-looking graphs - like the ones used in the news, by scientists, or in apps and websites.

❓Question

Why is Python a more powerful tool than Excel when we want to create lots of graphs?

Who uses this?

Data scientists use Python to help explain results to doctors, politicians, and business leaders
The code helps people make big decisions using data!

What you’ll do

You’ll make your own graphs using Python
Don’t worry if you’ve never used Python before - that’s OK!
We’ll give you code to start with
You’ll just edit parts of it to see what happens

⚠️ You might not understand everything in the code - and that’s totally normal! Even professional programmers don’t always understand it all straight away. Just have a go, and don’t be afraid to make mistakes.

Animal ageing

Some animals live really long lives. The oldest human ever was a French woman named Jeanne Calment - she lived to the age of 122 years and 164 days! That’s pretty impressive… but some animals can live even longer.

What’s their secret? Scientists are trying to find out - and if they do, it could help us understand how to live longer, healthier lives too. Below are some examples of long-lived animals:

In this activity, you’ll use Python to make plots showing how long different animals can live. You’ll get to customise your graphs and compare short- and long-lived species.

What you’ll learn

Some animals live way longer than others - we’ll find out which!
How to use Python to explore real data
How to make custom plots showing animal lifespans

Load packages

Python does not know how to process and plot data on its own. Python packages contain additional commands that don’t come installed with Python, and allow us to carry out certain extra tasks.

In this activity we’ll use two important ones:

pandas - lets python read and edit data (much like you’d use Excel to process raw data)
matplotlib - helps us draw graphs and plots

To use them, copy and paste the code below into your first code chunk, then press the play button:

Code


import pandas as pd
import matplotlib.pyplot as plt

❓Question

What does the pandas package help Python do?

As part of this code, we rename the packages to something shorter.matplotlib.pyplot is quite a lot of letters to type every time! Each time we drew a plot we’d have to type:

matplotlib.pyplot.plot

Instead, we can rename the package as we load it to something simple and easy to remember. In this case plt. So the above line of code would become:

plt.plot

In summary, this is how we load and rename a package:

❓Question

Why do we rename packages like matplotlib.pyplot to something shorter like plt?

Load the data

Next we are going to load our animal ageing data. The data we’re going to be using can be found here. We are going to load it using the package mentioned earlier ‘pandas’. Remember, we have loaded ‘pandas’ and renamed it to ‘pd’.

The code to load the data is below.

Code


data = pd.read_csv("https://raw.githubusercontent.com/CBFLivUni/scholars_event/refs/heads/main/data/animal_ageing_data.csv")

We use the read_csv function contained inside the ‘pandas’ package to load the data at the url. We then store it in a variable called ‘data’ using the equals sign.

We can view the data by typing the name of the variable in a code chunk:

Code

data

❓Question

What does this code do?

data = pd.read_csv("some_data.csv")

Inspect and format data

There are lots of rows and columns in our data. In the code below we will extract just the oldest animals from the data, and then we will print out their names and lifespans.

Code

# Find the top 8 longest living animals
oldest_animals = data.sort_values("Maximum longevity (yrs)", ascending=False).head(8)

print("\nThe 8 longest-living animals in this dataset are:")
print(oldest_animals[["Common name", "Maximum longevity (yrs)"]])


The 8 longest-living animals in this dataset are:

          Common name  Maximum longevity (yrs)
0   Ocean quahog clam                    507.0
1     Greenland shark                    392.0
2       Bowhead whale                    211.0
3   Rougheye rockfish                    205.0
4      Red sea urchin                    200.0
5  Galapagos tortoise                    177.0
6       Lake sturgeon                    152.0
7               Human                    122.5

In this code we take our data and sort it in order of longevity. We tell it NOT to do it in ascending order by saying ascending=False. We take the top 8 by including head(8). We then save it in a new variable called ‘oldest animals’.

After that, we print the two colums we are interested in (“Common name” and “Maximum longevity” (yrs)“). Do any of these animals surprise you?

❓Question

What does .sort_values(..., ascending=False) do in our code?

❓Question

What does .head(8) do?

Challenges

Can you modify the above code so that ‘oldest_animals’ has the 12 most long-lived animals instead?

What is the 10th longest lived animal?

Can you make another variable called ‘shortest_lived_animals’ that has the 12 most short-lived animals?

Which animal only lives for a maximum of 0.27 years?

Plot the data!

We are now going to create a simple horizontal bar plot of the 12 most long-lived animals. In the code below:

The first line sets the size of the figure.
The second line creates a horizontal bar plot (this is a normal bar plot rotated 90 degrees).
The third line tells Python to display the plot.

Code

plt.figure(figsize=(10, 6))
plt.barh(oldest_animals["Common name"], oldest_animals["Maximum longevity (yrs)"]) 
plt.show()

🤔 Why might we want to rotate the bars to be horizontal (barh) instead of vertical?

Try changing the code to use plt.bar(...) instead of plt.barh(...) and look at what happens to the animal names.

We rotate the bars to be horizontal so that the animal names appear on the y-axis. That way they don’t get squashed and are easier to read.

So far this is quite a simple plot, and it doesn’t have any labels or units for the x- and y- axes - this would be considered a poor graph in reality!

With matplotlib (our Python plotting package) we can keep adding layers of new information to our plot. We are now going to fill in the x- and y- axis. However, we have left the y-axis blank for you to fill in with a suitable name, for example ‘Animal’ or ‘Species’.

Code

plt.figure(figsize=(10, 6))
plt.barh(oldest_animals["Common name"], oldest_animals["Maximum longevity (yrs)"]) 
plt.xlabel("Maximum Lifespan (years)") 
plt.ylabel("______") # ← Label for y-axis
plt.show()

❓Question

Can you remember what plt.show() does?

We have now created a simple plot of animal longevity!

Make it pretty

Our graph above looks quite good but we might want to customise it more to our liking. We could first change the colours. We do this by passing an additional ‘argument’ to our command plt to let it know the colours we want to use. An example of changing all the colours of our graph is below. (Note: the spelling of ‘colour’ is American in python).

We have also added a title to our graph to explain what we are seeing.

Code

plt.figure(figsize=(10, 6))
plt.barh(oldest_animals["Common name"], oldest_animals["Maximum longevity (yrs)"], 
         color=["red", "orange", "yellow", "green", "blue", "purple", "pink", "brown", "gray", "cyan", "teal", "magenta"])
plt.xlabel("Maximum Lifespan (years)")
plt.ylabel("Animal")
plt.title("Top 12 Longest-Living Animals in the Data")
plt.show()

You can use any colours you like to customise the graph. You just have to change the name of the colours inside the ‘color’ argument. The ones below all come included with matplotlib.

As well as the colours that come included, matplotlib lets you to pick any colour using a hexcode (a 6 character number/letter code after a hash/#).

We can create hexcodes for colours using some of these links:

The last link will allow you to create a colour palette from an image and give you the hex codes for the palette.

Challenges

Can you change the colours of the graph using the inbuilt colour names in matplotlib?
Can you change the colours using hexcodes instead? Maybe you could pick colours that remind you of the animals? If you’re unsure what they look like then use Google to check. Otherwise, just select colours you like. ☺

Another way we can modify our graph is by using a different ‘theme’. Themes are different ways of styling the plot, for example background colour, default bar colours, fonts, gridlines, etc. Matplotlib comes with prepared styles that you can use to modify your graph.

We can then see a list of which styles we can use using this code:

Code

plt.style.available

['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'petroff10', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']

❓Question

Which of the following is a style you can use in matplotlib?

An example of one of the styles applied to our graph is below:

Code

plt.style.use('ggplot')

plt.figure(figsize=(10, 6))
plt.barh(oldest_animals["Common name"], oldest_animals["Maximum longevity (yrs)"])
plt.xlabel("Maximum Lifespan (years)")
plt.ylabel("Animal")
plt.title("Top 12 Longest-Living Animals in the Data")
plt.show()

Challenges

Pick a style! Apply it to your graph instead of the one used in the example above. Try a few, which do you like best?
Can you include a theme AND your custom colours?

If you don’t want to use a theme and prefer the default appearance, you can change it back at any time by running this block of code.

Code


plt.style.use('default')

To finish

Final challenge

Now, using everything we’ve learned, can you modify the code to create a plot of the shortest-lived animals in the dataset - remember that earlier in the activity we created a variable storing the data called shortest_lived_animals.

Don’t worry if this part proves tricky and ask the teachers for help at any point if you get stuck!

Well done! You have now learned how to create a plot of using Python!