pingouin.plot_paired#

pingouin.plot_paired(data=None, dv=None, within=None, subject=None, order=None, boxplot=True, boxplot_in_front=False, orient='v', ax=None, colors=['green', 'grey', 'indianred'], pointplot_kwargs={'marker': '.', 'scale': 0.6}, boxplot_kwargs={'color': 'lightslategrey', 'width': 0.2})[source]#

Paired plot.

Parameters:

datapandas.DataFrame: Long-format dataFrame.
dvstring: Name of column containing the dependent variable.
withinstring: Name of column containing the within-subject factor.
subjectstring: Name of column containing the subject identifier.
orderlist of str: List of values in within that define the order of elements on the x-axis of the plot. If None, uses alphabetical order.
boxplotboolean: If True, add a boxplot to the paired lines using the seaborn.boxplot() function.
boxplot_in_frontboolean: If True, the boxplot is plotted on the foreground (i.e. above the individual lines) and with a slight transparency. This makes the overall plot more readable when plotting a large numbers of subjects.

Added in version 0.3.8.
orientstring: Plot the boxplots vertically and the subjects on the x-axis if orient='v' (default). Set to orient='h' to rotate the plot by by 90 degrees.

Added in version 0.3.9.
axmatplotlib axes: Axis on which to draw the plot.
colorslist of str: Line colors names. Default is green when value increases from A to B, indianred when value decreases from A to B and grey when the value is the same in both measurements.
pointplot_kwargsdict: Dictionnary of optional arguments that are passed to the seaborn.pointplot() function.
boxplot_kwargsdict: Dictionnary of optional arguments that are passed to the seaborn.boxplot() function.

Returns:

axMatplotlib Axes instance: Returns the Axes object with the plot for further tweaking.

Notes

Data must be a long-format pandas DataFrame. Missing values are automatically removed using a strict listwise approach (= complete-case analysis).

Examples

Default paired plot:

>>> import pingouin as pg
>>> df = pg.read_dataset('mixed_anova').query("Time != 'January'")
>>> df = df.query("Group == 'Meditation' and Subject > 40")
>>> ax = pg.plot_paired(data=df, dv='Scores', within='Time', subject='Subject')

Paired plot on an existing axis (no boxplot and uniform color):

>>> import pingouin as pg
>>> import matplotlib.pyplot as plt
>>> df = pg.read_dataset('mixed_anova').query("Time != 'January'")
>>> df = df.query("Group == 'Meditation' and Subject > 40")
>>> fig, ax1 = plt.subplots(1, 1, figsize=(5, 4))
>>> pg.plot_paired(data=df, dv='Scores', within='Time',
...                subject='Subject', ax=ax1, boxplot=False,
...                colors=['grey', 'grey', 'grey'])  

Horizontal paired plot with three unique within-levels:

>>> import pingouin as pg
>>> import matplotlib.pyplot as plt
>>> df = pg.read_dataset('mixed_anova').query("Group == 'Meditation'")
>>> # df = df.query("Group == 'Meditation' and Subject > 40")
>>> pg.plot_paired(data=df, dv='Scores', within='Time',
...                subject='Subject', orient='h')  

With the boxplot on the foreground:

>>> import pingouin as pg
>>> df = pg.read_dataset('mixed_anova').query("Time != 'January'")
>>> df = df.query("Group == 'Control'")
>>> ax = pg.plot_paired(data=df, dv='Scores', within='Time',
...                     subject='Subject', boxplot_in_front=True)