Basic Figure Creation with Seaborn

To create a figure or graph, we are typically going to: * call some specific function within Seaborn, such as sns.lineplot(), to create a plotting object * include arguments in the call that specify the data to plot and any options that we need * write additional code to make tweaks to the object, such as adding a title, legend, or axis labels. * call plt.show() to display the object

Displaying a plot will typically involve a few lines of code. The initial call will create an object that we can't see yet, but that knows what type of plot it is and what data it holds. We then add to the object and tweak things. Finally, when we want to see what we've built, we can tell the object to show itself.

This is a slightly different way of interacting with objects than you might be familiar with. It takes some getting used to. The thing to remember is that when you call the initial function, you are creating an object. You can then do things with that object, such as putting it in a list, assigning it to a variable, or displaying it.

Note: Usually, plot objects don't display themselves until you ask, but Colab (and other forms of Jupyter notebooks) try to be helpful and may display your objects even when you haven't asked. You might be tempted to rely on that convenience, but I'd like you not to get into that habit, as it can cause quite a bit of confusion later down the road.


Seaborn Patterns

Let's look at some code to get a sense of the overall pattern for making plots with Seaborn. Note: Remember that sns is the alias for the Seaborn library.

# set figure size
sns.set(rc={'figure.figsize':(10,8)})

# data for our plot
yearlist = [1970, 1980, 1990, 2000, 2010, 2020, 2030]
zombiecount = [0, 0, 0, 0, 10, 20, 50] # slowest zombie spread of all time

# plot starts here
sns.lineplot(x = yearlist, y = zombiecount) # called a line plot, lists as x and y
plt.show() # displays the plot



Let's add a few more components to our figure.

# data for our plot
yearlist = [1970, 1980, 1990, 2000, 2010, 2020, 2030]
zombiecount = [0, 0, 0, 0, 10, 20, 50] # slowest zombie spread of all time

# plot starts here
sns.lineplot(x = yearlist, y = zombiecount) # called a line plot, lists as x and y
plt.title('Slow Zombie Apocalypse Chart') # added a title
plt.xlabel('Year') # added a x-axis label
plt.ylabel('Zombies') # added a y-axis label
plt.show() # displays the plot



Here we will plot two sets of data on the same figure. Notice we are calling sns.lineplot() twice, but we are not getting back two plots. Instead, the second call adds to the existing object.

# data for our plot
yearlist = [1970, 1980, 1990, 2000, 2010, 2020, 2030]
zombiecount = [0, 0, 0, 0, 10, 20, 50] # slowest zombie spread of all time
bunnycount = [45, 35, 25, 15, 10, 5, 1] # bunnies have a rough time

# plot starts here
sns.lineplot(x = yearlist,
             y = zombiecount,
             label = 'zombies') # called a line plot, lists as x and y
sns.lineplot(x = yearlist,
             y = bunnycount,
             label = 'bunnies') # add a line to the existing plot
plt.show() # displays the plot



Again, here is the general pattern:

  1. Have the data in some form

  2. Call a Seaborn function to plot the data and supply necessary arguments

  3. Tweak or add to the plot with calls to plt (which is an alias for another library called matplotlib)

  4. Display the result

Make sure you can recognize how the code above accomplishes each step.


Seaborn Patterns with Pandas

If you have your data in a dataframe, you can hand specific series from your dataframe to Seaborn for plotting.

For example:

# create a zombie bunny dataframe
# don't worry about how this works
dfzb = pd.DataFrame(zip(yearlist, zombiecount, bunnycount))
dfzb.columns = ['year', 'zombies', 'bunnies']

# plot the data
sns.lineplot(x = 'year', y = 'zombies', data = dfzb) # specifies series and dataframe
plt.show()



Notice that Seaborn has taken the series names and used them as axis labels. We can override this with a subsequent call to plt.

sns.lineplot(x = 'year', y = 'zombies', data = dfzb) # specify series and dataframe
plt.ylabel('Zombies!!!!')
plt.show()



We have options for supplying our data to Seaborn. We can feed it the data directly, for example, by passing it into a series, or we use data = to specify a dataframe, and then it will look for x and y in the column names. Students commonly blend the two approaches and have difficulty figuring out why it fails. Always check that you've specified a data source if you are trying to plot a series from a dataframe.