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# Filled Area Plots in matplotlib

How to make a filled area plot in matplotlib. An area chart displays a solid color between the traces of a graph.

#### New to Plotly?¶

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We also have a quick-reference cheatsheet (new!) to help you get started!

#### Version Check¶

Plotly's python package is updated frequently. Run pip install plotly --upgrade to use the latest version.

In [1]:
import plotly
plotly.__version__

Out[1]:
'3.1.1'

#### Area Plot¶

In [2]:
import plotly.plotly as py
import plotly.tools as tls

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([2,1,3,1,2])

update = {'data':[{'fill': 'tozeroy'}]}  # this updates the trace
plotly_fig = tls.mpl_to_plotly( fig )
plotly_fig.update(update)
py.iplot(plotly_fig, update=update, filename='mpl-basic-area')

Out[2]:

#### Multiple Line Area Plot¶

In [3]:
import plotly.plotly as py
import plotly.tools as tls

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2*np.pi, 100)
fig, ax = plt.subplots()
ax.plot(np.sin(x), label='sin'); ax.plot(np.cos(x), label='cos')

update = {'data':[{'fill': 'tozeroy'}]}  # this updates BOTH traces now
plotly_fig = tls.mpl_to_plotly( fig )
plotly_fig.update(update)
py.iplot(plotly_fig, filename='mpl-multi-fill')

Out[3]:

#### Stacked Line Plot¶

In [4]:
import plotly.plotly as py
import plotly.tools as tls

import numpy as np
import matplotlib.pyplot as plt

# create our stacked data manually
y0 = np.random.rand(100)
y1 = y0 + np.random.rand(100)
y2 = y1 + np.random.rand(100)
capacity = 3*np.ones(100)

# make the mpl plot (no fill yet)
fig, ax = plt.subplots()
ax.plot(y0, label='y0')
ax.plot(y1, label='y1')
ax.plot(y2, label='y2')
ax.plot(capacity, label='capacity')

# set all traces' "fill" so that it fills to the next 'y' trace
update = {'data':[{'fill': 'tonexty'}]}

# strip style just lets Plotly make the styling choices (e.g., colors)
plotly_fig = tls.mpl_to_plotly( fig )
plotly_fig.update(update)
py.iplot(plotly_fig, strip_style=True, filename='mpl-stacked-line')

Out[4]:

#### Reference¶

See https://plot.ly/python/reference/ for more information and chart attribute options!

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