Making Plots With plotnine

Last updated on 2024-03-29 | Edit this page

Estimated time: 90 minutes

Overview

Questions

  • How can I visualize data in Python?
  • What is ‘grammar of graphics’?

Objectives

  • Create a plotnine object.
  • Set universal plot settings.
  • Modify an existing plotnine object.
  • Change the aesthetics of a plot such as color.
  • Edit the axis labels.
  • Build complex plots using a step-by-step approach.
  • Create scatter plots, box plots, and time series plots.
  • Use the facet_wrap and facet_grid commands to create a collection of plots splitting the data by a factor variable.
  • Create customized plot styles to meet their needs.

Disclaimer

Python has powerful built-in plotting capabilities such as matplotlib, but for this episode, we will be using the plotnine package, which facilitates the creation of highly-informative plots of structured data based on the R implementation of ggplot2 and The Grammar of Graphics by Leland Wilkinson. The plotnine package is built on top of Matplotlib and interacts well with Pandas.

Reminder

plotnine is not included in the standard Anaconda installation and needs to be installed separately. If you haven’t done so already, you can find installation instructions on the Setup page.

Just as with the other packages, plotnine needs to be imported. It is good practice to not just load an entire package such as from plotnine import *, but to use an abbreviation as we used pd for Pandas:

PYTHON 

%matplotlib inline
import plotnine as p9

From now on, the functions of plotnine are available using p9.. For the exercise, we will use the surveys.csv data set, with the NA values removed

PYTHON 

import pandas as pd

surveys_complete = pd.read_csv('data/surveys.csv')
surveys_complete = surveys_complete.dropna()

If learners have trouble generating the output, or anything happens with that, the folder sample_output in this repository contains the file surveys_complete.csv with the data they should generate.

Plotting with plotnine

The plotnine package (cfr. other packages conform The Grammar of Graphics) supports the creation of complex plots from data in a dataframe. It uses default settings, which help creating publication quality plots with a minimal amount of settings and tweaking.

plotnine graphics are built step by step by adding new elements adding different elements on top of each other using the + operator. Putting the individual steps together in brackets () provides Python-compatible syntax.

To build a plotnine graphic we need to:

  • Bind the plot to a specific data frame using the data argument:

PYTHON 

(p9.ggplot(data=surveys_complete))

Note plotnine contains a lot of deprecation warnings in some versions of python/matplotlib, warnings may need to be suppressed with

PYTHON 

import warnings
warnings.filterwarnings(action='once')

As we have not defined anything else, just an empty figure is available and presented.

  • Define aesthetics (aes), by selecting variables used in the plot and mapping them to a presentation such as plotting size, shape, color, etc. You can interpret this as: which of the variables will influence the plotted objects/geometries:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length')))

The most important aes mappings are: x, y, alpha, color, colour, fill, linetype, shape, size and stroke.

  • Still no specific data is plotted, as we have to define what kind of geometry will be used for the plot. The most straightforward is probably using points. Points is one of the geoms options, the graphical representation of the data in the plot. Others are lines, bars,… To add a geom to the plot use + operator:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length'))
    + p9.geom_point()
)

The + in the plotnine package is particularly useful because it allows you to modify existing plotnine objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this:

PYTHON 

# Create
surveys_plot = p9.ggplot(data=surveys_complete,
                         mapping=p9.aes(x='weight', y='hindfoot_length'))

# Draw the plot
surveys_plot + p9.geom_point()
scatter plot of hindfoot length vs weight with black dots denoting individual sampled animals, showing 4 main clusters of dots in the middle and left middle sides

Challenge - bar chart

Working on the surveys_complete data set, use the plot-id column to create a bar-plot that counts the number of records for each plot. (Check the documentation of the bar geometry to handle the counts)

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='plot_id'))
    + p9.geom_bar()
)
bar chart of count of rodents caught at each plot site

Notes:

  • Anything you put in the ggplot() function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x and y axis you set up in aes().
  • You can also specify aesthetics for a given geom independently of the aesthetics defined globally in the ggplot() function.

Building your plots iteratively

Building plots with plotnine is typically an iterative process. We start by defining the dataset we’ll use, lay the axes, and choose a geom. Hence, the data, aes and geom-* are the elementary elements of any graph:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length'))
    + p9.geom_point()
)

Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to avoid overplotting:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length'))
    + p9.geom_point(alpha=0.1)
)
scatter plot of hindfoot-length vs weight of rodents, showing a curve increasing to a plateau

We can also add colors for all the points

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length'))
    + p9.geom_point(alpha=0.1, color='blue')
)
scatter plot of hindfoot-length vs weight of rodents, demonstrating overplotting

Or to color each species in the plot differently, map the species_id column to the color aesthetic:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight',
                          y='hindfoot_length',
                          color='species_id'))
    + p9.geom_point(alpha=0.1)
)
scatter plot of Hindfoot length vs weight with colors coordinating to specific species, showing abundance in the mid to lower left side of the plot

Apart from the adaptations of the arguments and settings of the data, aes and geom-* elements, additional elements can be added as well, using the + operator:

  • Changing the labels:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length', color='species_id'))
    + p9.geom_point(alpha=0.1)
    + p9.xlab("Weight (g)")
)
scatter plot of Hindfoot length vs weight (g) with colors coordinating to specific species, showing abundance in the mid to lower left side of the plot
  • Defining scale for colors, axes,… For example, a log-version of the x-axis could support the interpretation of the lower numbers:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length', color='species_id'))
    + p9.geom_point(alpha=0.1)
    + p9.xlab("Weight (g)")
    + p9.scale_x_log10()
)
Scatterplot of hindfoot length versus weight where logarithmic x-axis is used to distribute lower numbers
  • Changing the theme (theme_*) or some specific theming (theme) elements. Usually plots with white background look more readable when printed. We can set the background to white using the function theme_bw().

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length', color='species_id'))
    + p9.geom_point(alpha=0.1)
    + p9.xlab("Weight (g)")
    + p9.scale_x_log10()
    + p9.theme_bw()
    + p9.theme(text=p9.element_text(size=16))
)
Scatterplot of hindfoot length versus weight on a logarithmic x-axis using a white background

Challenge - Bar plot adaptations

Adapt the bar plot of the previous exercise by mapping the sex variable to the color fill of the bar chart. Change the scale of the color fill by providing the colors blue and orange manually (see API reference to find the appropriate function).

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='plot_id',
                          fill='sex'))
    + p9.geom_bar()
    + p9.scale_fill_manual(["blue", "orange"])
)
Bar chart of counts of males (yellow) and females (blue) vs plot id, showing the number of females to be higher in all plots

Plotting distributions

Visualizing distributions is a common task during data exploration and analysis. To visualize the distribution of weight within each species_id group, a boxplot can be used:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='species_id',
                          y='weight'))
    + p9.geom_boxplot()
)
boxplot showing distribution of rodent weight for each species group

By adding points of the individual observations to the boxplot, we can have a better idea of the number of measurements and of their distribution:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='species_id',
                          y='weight'))
    + p9.geom_jitter(alpha=0.2)
    + p9.geom_boxplot(alpha=0.)
)
Boxplot of weight by species overlaying observation points to visualize the distribution

Challenge - distributions

Boxplots are useful summaries, but hide the shape of the distribution. For example, if there is a bimodal distribution, this would not be observed with a boxplot. An alternative to the boxplot is the violin plot (sometimes known as a beanplot), where the shape (of the density of points) is drawn.

In many types of data, it is important to consider the scale of the observations. For example, it may be worth changing the scale of the axis to better distribute the observations in the space of the plot.

  • Replace the box plot with a violin plot, see geom_violin()
  • Represent weight on the log10 scale, see scale_y_log10()
  • Add color to the datapoints on your boxplot according to the plot from which the sample was taken (plot_id)

Hint: Check the class for plot_id. By using factor() within the aes mapping of a variable, plotnine will handle the values as category values.

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='species_id',
                          y='weight',
                          color='factor(plot_id)'))
    + p9.geom_jitter(alpha=0.3)
    + p9.geom_violin(alpha=0, color="0.7")
    + p9.scale_y_log10()
)
Violin plot of weight of species shown with weight scaled down and datapoints with color.

Plotting time series data

Let’s calculate the number of counts per year for each species. To do that we need to group data first and count the species (species_id) within each group.

PYTHON 

yearly_counts = surveys_complete.groupby(['year', 'species_id'])['species_id'].count()
yearly_counts

When checking the result of the previous calculation, we actually have both the year and the species_id as a row index. We can reset this index to use both as column variable:

PYTHON 

yearly_counts = yearly_counts.reset_index(name='counts')
yearly_counts

Timelapse data can be visualised as a line plot (geom_line) with years on x axis and counts on the y axis.

PYTHON 

(p9.ggplot(data=yearly_counts,
           mapping=p9.aes(x='year',
                          y='counts'))
    + p9.geom_line()
)

Unfortunately this does not work, because we plot data for all the species together. We need to tell plotnine to draw a line for each species by modifying the aesthetic function and map the species_id to the color:

PYTHON 

(p9.ggplot(data=yearly_counts,
           mapping=p9.aes(x='year',
                          y='counts',
                          color='species_id'))
    + p9.geom_line()
)
Line graph of count per year where data for each species is indicated by a different color

Faceting

As any other library supporting the Grammar of Graphics, plotnine has a special technique called faceting that allows to split one plot into multiple plots based on a factor variable included in the dataset.

Consider our scatter plot of the weight versus the hindfoot_length from the previous sections:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight',
                          y='hindfoot_length',
                          color='species_id'))
    + p9.geom_point(alpha=0.1)
)

We can now keep the same code and at the facet_wrap on a chosen variable to split out the graph and make a separate graph for each of the groups in that variable. As an example, use sex:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight',
                          y='hindfoot_length',
                          color='species_id'))
    + p9.geom_point(alpha=0.1)
    + p9.facet_wrap("sex")
)
2 scatter plots, one for males and the other for females, of hindfoot length vs weight with colored dots denoting specific species, showing the trend is the same between both male and females of multiple species

We can apply the same concept on any of the available categorical variables:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight',
                          y='hindfoot_length',
                          color='species_id'))
    + p9.geom_point(alpha=0.1)
    + p9.facet_wrap("plot_id")
)
24 individual scatter plots of Hindfoot length vs weight of species with colors denoting species and numbers above plot representing 1 of the 24 plots, showing trends for each unique plot id studied

The facet_wrap geometry extracts plots into an arbitrary number of dimensions to allow them to cleanly fit on one page. On the other hand, the facet_grid geometry allows you to explicitly specify how you want your plots to be arranged via formula notation (rows ~ columns; a . can be used as a placeholder that indicates only one row or column).

PYTHON 

# only select the years of interest
survey_2000 = surveys_complete[surveys_complete["year"].isin([2000, 2001])]

(p9.ggplot(data=survey_2000,
           mapping=p9.aes(x='weight',
                          y='hindfoot_length',
                          color='species_id'))
    + p9.geom_point(alpha=0.1)
    + p9.facet_grid("year ~ sex")
)
Set of 4 color scatterplots showing the relationship between weight and hind foot length for 13 species, separated by sex and year

Challenge - facetting

Create a separate plot for each of the species that depicts how the average weight of the species changes through the years.

PYTHON 

yearly_weight = surveys_complete.groupby(['year', 'species_id'])['weight'].mean().reset_index()
(p9.ggplot(data=yearly_weight, mapping=p9.aes(x='year', y='weight'))
    + p9.geom_line()
    + p9.facet_wrap("species_id")
)

Challenge - facetting

Based on the previous exercise, visually compare how the weights of male and females has changed through time by creating a separate plot for each sex and an individual color assigned to each species_id.

PYTHON 

yearly_weight = surveys_complete.groupby(['year', 'species_id', 'sex'])['weight'].mean().reset_index()

(p9.ggplot(data=yearly_weight,
           mapping=p9.aes(x='year',
                          y='weight',
                          color='species_id'))
    + p9.geom_line()
    + p9.facet_wrap("sex")
)

Further customization

As the syntax of plotnine follows the original R package ggplot2, the documentation of ggplot2 can provide information and inspiration to customize graphs. Take a look at the ggplot2 cheat sheet, and think of ways to improve the plot. You can write down some of your ideas as comments in the Etherpad.

The theming options provide a rich set of visual adaptations. Consider the following example of a bar plot with the counts per year.

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='factor(year)'))
    + p9.geom_bar()
)
Bar graph of count per year showing overlapping x-axis labels

Notice that we use the year here as a categorical variable by using the factor functionality. However, by doing so, we have the individual year labels overlapping with each other. The theme functionality provides a way to rotate the text of the x-axis labels:

PYTHON 

(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='factor(year)'))
    + p9.geom_bar()
    + p9.theme_bw()
    + p9.theme(axis_text_x = p9.element_text(angle=90))
)
Bar graph of count per year demonstrating how the theme function rotates the x-axis labels

When you like a specific set of theme-customizations you created, you can save them as an object to easily apply them to other plots you may create:

PYTHON 

my_custom_theme = p9.theme(axis_text_x = p9.element_text(color="grey", size=10,
                                                         angle=90, hjust=.5),
                           axis_text_y = p9.element_text(color="grey", size=10))
(p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='factor(year)'))
    + p9.geom_bar()
    + my_custom_theme
)
Bar graph of count per year demonstrating the use of a customized theme

Challenge - customization

Please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own.

Here are some ideas:

After creating your plot, you can save it to a file in your favourite format. You can easily change the dimension (and its resolution) of your plot by adjusting the appropriate arguments (width, height and dpi):

PYTHON 

my_plot = (p9.ggplot(data=surveys_complete,
           mapping=p9.aes(x='weight', y='hindfoot_length'))
    + p9.geom_point()
)
my_plot.save("scatterplot.png", width=10, height=10, dpi=300)

Key Points

  • The data, aes variables and a geometry are the main elements of a plotnine graph
  • With the + operator, additional scale_*, theme_*, xlab/ylab and facet_* elements are added