In [1]:

    

import numpy as np
import matplotlib.pyplot as plt


def fnx():
    return np.random.randint(5, 50, 10)

y = np.row_stack((fnx(), fnx(), fnx()))
x = np.arange(10)

y1, y2, y3 = fnx(), fnx(), fnx()

fig, ax = plt.subplots()
ax.stackplot(x, y)
plt.show()

fig, ax = plt.subplots()
ax.stackplot(x, y1, y2, y3)
plt.show()


    

In [3]:

    

print(ax.stackplot.__doc__)


    

    




Draws a stacked area plot.

    *x* : 1d array of dimension N

    *y* : 2d array of dimension MxN, OR any number 1d arrays each of dimension
          1xN. The data is assumed to be unstacked. Each of the following
          calls is legal::

            stackplot(x, y)               # where y is MxN
            stackplot(x, y1, y2, y3, y4)  # where y1, y2, y3, y4, are all 1xNm

    Keyword arguments:

    *baseline* : ['zero', 'sym', 'wiggle', 'weighted_wiggle']
                Method used to calculate the baseline. 'zero' is just a
                simple stacked plot. 'sym' is symmetric around zero and
                is sometimes called `ThemeRiver`.  'wiggle' minimizes the
                sum of the squared slopes. 'weighted_wiggle' does the
                same but weights to account for size of each layer.
                It is also called `Streamgraph`-layout. More details
                can be found at http://leebyron.com/streamgraph/.


    *labels* : A list or tuple of labels to assign to each data series.


    *colors* : A list or tuple of colors. These will be cycled through and
               used to colour the stacked areas.
               All other keyword arguments are passed to
               :func:`~matplotlib.Axes.fill_between`

    Returns *r* : A list of
    :class:`~matplotlib.collections.PolyCollection`, one for each
    element in the stacked area plot.
    

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