Visualising results

One thing missing in a lot of corpus linguistic tools is the ability to produce high-quality visualisations of corpus data. corpkit uses the corpkit.interrogation.Interrogation.visualise method to do this.


Most of the keyword arguments from Pandas’ plot method are available. See their documentation for more information.


visualise() is a method of all corpkit.interrogation.Interrogation objects. If you use from corpkit import *, it is also monkey-patched to Pandas objects.


If you’re using a Jupyter Notebook, make sure you use %matplotlib inline or %matplotlib notebook to set the appropriate backend.

A common workflow is to interrogate a corpus, relative results, and visualise:

>>> from corpkit import *
>>> corpus = Corpus('data/P-parsed', load_saved=True)
>>> counts = corpus.interrogate({T: r'MD < __'})
>>> reldat = counts.edit('%', SELF)
>>> reldat.visualise('Modals', kind='line', num_to_plot=ALL).show()
### the visualise method can also attach to the df:
>>> reldat.results.visualise(...).show()

The current behaviour of visualise() is to return the pyplot module. This allows you to edit figures further before showing them. Therefore, there are two ways to show the figure:

>>> data.visualise().show()
>>> plt = data.visualise()

Plot type

The visualise method allows line, bar, horizontal bar (barh), area, and pie charts. Those with seaborn can also use 'heatmap' (docs). Just pass in the type as a string with the kind keyword argument. Arguments such as robust=True can then be used.

>>> data.visualise(kind='heatmap', robust=True, figsize=(4,12),
...                x_label='Subcorpus', y_label='Event').show()

Heatmap example

Stacked area/line plots can be made with stacked=True. You can also use filled=True to attempt to make all values sum to 100. Cumulative plotting can be done with cumulative=True. Below is an area plot beside an area plot where filled=True. Both use the vidiris colour scheme.

../../_images/area.png ../../_images/area-filled.png

Plot style

You can select from a number of styles, such as ggplot, fivethirtyeight, bmh, and classic. If you have seaborn installed (and you should), then you can also select from seaborn styles (seaborn-paper, seaborn-dark, etc.).

Figure and font size

You can pass in a tuple of (width, height) to control the size of the figure. You can also pass an integer as fontsize.

Title and labels

You can label your plot with title, x_label and y_label:

>>> data.visualise('Modals', x_label='Subcorpus', y_label='Relative frequency')


subplots=True makes a separate plot for every entry in the data. If using it, you’ll probably also want to use layout=(rows,columns) to specify how you’d like the plots arranged.

>>> data.visualise(subplots=True, layout=(2,3)).show()

Line charts using subplots and layout specification


If you have LaTeX installed, you can use tex=True to render text with LaTeX. By default, visualise() tries to use LaTeX if it can.


You can turn the legend off with legend=False. Legend placement can be controlled with legend_pos, which can be:

Margin Figure Margin
outside upper left upper left upper right outside upper right
outside center left center left center right outside center right
outside lower left lower left lower right outside lower right

The default value, 'best', tries to find the best place automatically (without leaving the figure boundaries).

If you pass in draggable=True, you should be able to drag the legend around the figure.


You can use the colours keyword argument to pass in:

  1. A colour name recognised by matplotlib
  2. A hex colour string
  3. A colourmap object

There is an extra argument, black_and_white, which can be set to True to make greyscale plots. Unlike colours, it also updates line styles.

Saving figures

To save a figure to a project’s images directory, you can use the save argument. output_format='png'/'pdf' can be used to change the file format.

>>> data.visualise(save='name', output_format='png')

Other options

There are a number of further keyword arguments for customising figures:

Argument Type Action
grid bool Show grid in background
rot int Rotate x axis labels n degrees
shadow bool Shadows for some parts of plot
ncol int n columns for legend entries
explode list Explode these entries in pie
partial_pie bool Allow plotting of pie slices
legend_frame bool Show frame around legend
legend_alpha float Opacity of legend
reverse_legend bool Reverse legend entry order
transpose bool Flip axes of DataFrame
logx/logy bool Log scales
show_p_val bool Try to show p value in legend
interactive bool Experimental mpld3 use

A number of these and other options for customising figures are also described in the corpkit.interrogation.Interrogation.visualise method documentation.


The corpkit.interrogation.Interrogation also comes with a corpkit.interrogation.Interrogation.multiplot method, which can be used to show two different kinds of chart within the same figure.

The first two arguments for the function are two dict objects, which configure the larger and smaller plots.

For the second dictionary, you may pass in a data argument, which is an corpkit.interrogation.Interrogation or similar, and will be used as separate data for the subplots. This is useful, for example, if you want your main plot to show absolute frequencies, and your subplots to show relative frequencies.

There is also layout, which you can use to choose an overall grid design. You can also pass in a list of tuples if you like, to use your own layout. Below is a complete example, focussing on objects in risk processes:

>>> from corpkit import *
>>> from corpkit.dictionaries import *
### parse a collection of text files
>>> corpora = Corus('data/news')
### make dependency parse query: get get 'object' of risk process
>>> query = {F: roles.participant2, GL: r'\brisk', GF: roles.process}
### interrogate corpus, return lemma form, no coreference
>>> result = corpus.interrogate(query, show=[L], coref=False)
### generate relative frequencies, skip closed class, and sort
>>> inc = result.edit('%', SELF,
>>>                   sort_by='increase',
>>>                   skip_entries=wordlists.closedclass)
### visualise as area and line charts combined
>>> inc.multiplot({'title': 'Objects of risk processes, increasing',
>>>                'kind': 'area',
>>>                'x_label': 'Year',
>>>                'y_label': 'Percentage of all results'},
>>>                {'kind': 'line'}, layout=5)

multiplot example