Plotting

Matplotlib#

An essential feature of Jupyter Notebooks is of course the ability to visualize data and results via plots. A full guide to plotting in Python is beyond the scope of this course, but we'll offer a few glimpses into the plotting landscape of Python.

First of all, Python has a library for plotting called matplotlib, which comes packed with functionality for creating high-quality plots. Below is an example of how to generate a line plot of a sine wave.

# Import packages
import numpy as np
import matplotlib.pyplot as plt
# Generate a set of evenly spaced numbers between 0 and 100
x = np.linspace(0,3*np.pi,100)
# Use the sine function to generate y-values
y = np.sin(x)
# Plot the data
line, = plt.plot(x, y, color='red', linestyle="-")

By default plots are rendered in the notebook as rasterized images which can make the quality poor. To render in scalable vector graphics format use the set_matplotlib_formats from the matplotlib_inline package:

import matplotlib_inline
matplotlib_inline.backend_inline.set_matplotlib_formats('pdf', 'svg')

Now try running the code for the sine wave plot again.

Other packages for plotting#

Seaborn#

As we mentioned Matplotlib comes with a lot of functionality which is great because it allows you to create all sorts of plots and modify them exactly to your liking. However, this can also mean that creating very basic plots might involve a lot of cumbersome coding, when all you want is a simple bar chart!

Fortunately there are a number of Python packages that build upon matplotlib but with a much simplified interface. One such popular package is seaborn. Below we'll see how to generate a nice looking bar plot with error bars.

First import the seaborn package (using an abbreviated name to simplify typing):

import seaborn as sns

Next we'll load some example data of penguins collected at the Palmer Station, in Antarctica.

penguins = sns.load_dataset("penguins")
# Look at first 5 lines of the data
penguins.head(5)

The most basic way to generate a bar plot of this data with seaborn is:

sns.barplot(data=penguins)

Simple right? Yes, but maybe not very informative. Here seaborn simply calculates the mean of all numeric variables for the penguins and plots them with error bars representing a 95% confidence interval.

Let's say that instead we want to plot the mean value of the body mass of the penguins at the different islands where they were examined.

sns.barplot(data=penguins, x="island", y="body_mass_g", ci="sd", errwidth=.5);

Here we specified to use values in the 'island' column as categories for the x-axis, and values in the 'body_mass_g' column as values for the y-axis. The barplot function of seaborn will then calculate the mean body mass for each island and plot the bars. With ci="sd" we tell the function to draw the standard deviation as error bars, instead of computing a confidence interval. Finally errwidth=.5 sets the linewidth of the error bars.

If we instead want to visualize the data as a scatterplot we can use the sns.scatterplot function. Let's plot the body mass vs. bill length for all penguins and color the data points by species. We'll also move the legend outside of the plotting area and modify the x and y-axis labels:

# Store the matplotlib axes containing the plot in a variable called 'ax'
ax = sns.scatterplot(data=penguins, x="bill_length_mm", y="body_mass_g",
                     hue="species")
# Modify the labels of the plot
ax.set_xlabel("Bill length (mm)")
ax.set_ylabel("Body mass (g)")
# Set legend position outside of plot
ax.legend(bbox_to_anchor=(1,1));

If you want to save a plot to file you can use the plt.savefig function. Add the following to the bottom of the cell with the scatterplot code:

plt.savefig("scatterplot.pdf", bbox_inches="tight")

The bbox_inches="tight" setting ensures that the figure is not clipped when saved to file.

The Seaborn website contains great tutorials and examples of other ways to plot data!

Plotly#

Another package? Do you know plotly? The plotly Python library is a versatile and user-friendly plotting tool that offers a rich selection of over 40 chart types, including statistical, financial, geographic, scientific, and 3-dimensional options, enabling users to create interactive and engaging visualizations. It is also an open-source library, making it accessible to anyone who wants to use it.

What's the most important thing about it compared to the others? Its dynamic side! It is possible to zoom in, to hover over a point to obtain information, etc.

A classic scatter plot

from plotly.offline import init_notebook_mode, iplot
import plotly.express as px

init_notebook_mode(connected=True)   # initiate notebook for offline plot

fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
fig.show()

A bubble plot

df = px.data.gapminder()

fig = px.scatter(df.query("year==2007"), x="gdpPercap", y="lifeExp",
             size="pop", color="continent",
                 hover_name="country", log_x=True, size_max=60)
fig.show()

The Plotly website contains great tutorials and examples of other ways to plot data! And it's also available for R, JS, Julia and MATLAB®.