matplotlib.axes.Axes.bxp

Axes.bxp(bxpstats, positions=None, widths=None, vert=True, patch_artist=False, shownotches=False, showmeans=False, showcaps=True, showbox=True, showfliers=True, boxprops=None, whiskerprops=None, flierprops=None, medianprops=None, capprops=None, meanprops=None, meanline=False, manage_ticks=True, zorder=None)

Drawing function for box and whisker plots.

Make a box and whisker plot for each column of x or each vector in sequence x. The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers.

Parameters:

bxpstatslist of dicts

A list of dictionaries containing stats for each boxplot. Required keys are:

• med: The median (scalar float).
• q1: The first quartile (25th percentile) (scalar float).
• q3: The third quartile (75th percentile) (scalar float).
• whislo: Lower bound of the lower whisker (scalar float).
• whishi: Upper bound of the upper whisker (scalar float).

Optional keys are:

• mean: The mean (scalar float). Needed if showmeans=True.
• fliers: Data beyond the whiskers (sequence of floats). Needed if showfliers=True.
• cilo & cihi: Lower and upper confidence intervals about the median. Needed if shownotches=True.
• label: Name of the dataset (string). If available, this will be used a tick label for the boxplot

positionsarray-like, default: [1, 2, ..., n]

Sets the positions of the boxes. The ticks and limits are automatically set to match the positions.

widthsarray-like, default: None

Either a scalar or a vector and sets the width of each box. The default is 0.15*(distance between extreme positions), clipped to no less than 0.15 and no more than 0.5.

vertbool, default: True

If True (default), makes the boxes vertical. If False, makes horizontal boxes.

patch_artistbool, default: False

If False produces boxes with the Line2D artist. If True produces boxes with the Patch artist.

shownotchesbool, default: False

If False (default), produces a rectangular box plot. If True, will produce a notched box plot

showmeansbool, default: False

If True, will toggle on the rendering of the means

showcapsbool, default: True

If True, will toggle on the rendering of the caps

showboxbool, default: True

If True, will toggle on the rendering of the box

showfliersbool, default: True

If True, will toggle on the rendering of the fliers

boxpropsdict or None (default)

If provided, will set the plotting style of the boxes

whiskerpropsdict or None (default)

If provided, will set the plotting style of the whiskers

cappropsdict or None (default)

If provided, will set the plotting style of the caps

flierpropsdict or None (default)

If provided will set the plotting style of the fliers

medianpropsdict or None (default)

If provided, will set the plotting style of the medians

meanpropsdict or None (default)

If provided, will set the plotting style of the means

meanlinebool, default: False

If True (and showmeans is True), will try to render the mean as a line spanning the full width of the box according to meanprops. Not recommended if shownotches is also True. Otherwise, means will be shown as points.

manage_ticksbool, default: True

If True, the tick locations and labels will be adjusted to match the boxplot positions.

zorderfloat, default: Line2D.zorder = 2

The zorder of the resulting boxplot.

Returns:

dict

A dictionary mapping each component of the boxplot to a list of the Line2D instances created. That dictionary has the following keys (assuming vertical boxplots):

• boxes: the main body of the boxplot showing the quartiles and the median's confidence intervals if enabled.
• medians: horizontal lines at the median of each box.
• whiskers: the vertical lines extending to the most extreme, non-outlier data points.
• caps: the horizontal lines at the ends of the whiskers.
• fliers: points representing data that extend beyond the whiskers (fliers).
• means: points or lines representing the means.

Examples

(Source code, png, pdf)

(png, pdf)

Examples using matplotlib.axes.Axes.bxp