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Pandas plotting backend in Python

Plotly Express can be used as a Pandas .plot() backend.

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The popular Pandas data analysis and manipulation tool provides plotting functions on its DataFrame and Series objects, which have historically produced matplotlib plots. Since version 0.25, Pandas has provided a mechanism to use different backends, and as of version 4.8 of plotly, you can now use a Plotly Express-powered backend for Pandas plotting. This means you can now produce interactive plots directly from a data frame, without even needing to import Plotly.

To activate this backend, you will need to have Plotly installed, and then just need to set pd.options.plotting.backend to "plotly" and call .plot() to get a plotly.graph_objects.Figure object back, just like if you had called Plotly Express directly:

In [1]:

import pandas as pd
pd.options.plotting.backend = "plotly"

df = pd.DataFrame(dict(a=[1,3,2], b=[3,2,1]))
fig = df.plot()
fig.show()

In [2]:

import pandas as pd
pd.options.plotting.backend = "plotly"

df = pd.DataFrame(dict(a=[1,3,2], b=[3,2,1]))
fig = df.plot(title="Pandas Backend Example", template="simple_white",
              labels=dict(index="time", value="money", variable="option"))
fig.update_yaxes(tickprefix="$")
fig.show()

The Plotly plotting backend for Pandas is not intended to be a drop-in replacement for the default; it does not implement all or even most of the same keyword arguments, such as subplots=True etc.

The Plotly plotting backend for Pandas is a more convenient way to invoke certain Plotly Express functions by chaining a .plot() call without having to import Plotly Express directly. Plotly Express, as of version 4.8 with wide-form data support in addition to its robust long-form data support, implements behaviour for the x and y keywords that are very similar to the matplotlib backend.

In practice, this means that the following two ways of making a chart are identical and support the same additional arguments, because they call the same underlying code:

In [3]:

import pandas as pd
pd.options.plotting.backend = "plotly"
df = pd.DataFrame(dict(a=[1,3,2], b=[3,2,1]))

# using Plotly Express via the Pandas backend
fig1 = df.plot.bar()
fig1.show()

# using Plotly Express directly
import plotly.express as px
fig2 = px.bar(df)
fig2.show()

In [4]:

import pandas as pd
pd.options.plotting.backend = "plotly"
df = pd.DataFrame(dict(a=[1,3,2], b=[3,2,1]))

fig = df.plot.bar(facet_row="variable")
fig.show()

The Plotly backend supports the following kinds of Pandas plots: scatter, line, area, bar, barh, hist and box, via the call pattern df.plot(kind='scatter') or df.plot.scatter(). These delegate to the corresponding Plotly Express functions. In addition, the following are valid options to the kind argument of df.plot(): violin, strip, funnel, density_heatmap, density_contour and imshow, even though the call pattern df.plot.violin() is not supported for these kinds of charts, per the Pandas API.

In [5]:

import pandas as pd
import numpy as np
pd.options.plotting.backend = "plotly"
np.random.seed(1)

df = pd.DataFrame(dict(
    a=np.random.normal(loc=1, scale=2, size=100),
    b=np.random.normal(loc=2, scale=1, size=100)
))
fig = df.plot.scatter(x="a", y="b")
fig.show()

In [6]:

import pandas as pd
pd.options.plotting.backend = "plotly"

df = pd.DataFrame(dict(a=[1,3,2], b=[3,2,1]))
fig = df.plot.line()
fig.show()

In [7]:

import pandas as pd
pd.options.plotting.backend = "plotly"

df = pd.DataFrame(dict(a=[1,3,2], b=[3,2,1]))
fig = df.plot.area()
fig.show()

In [8]:

import pandas as pd
pd.options.plotting.backend = "plotly"

df = pd.DataFrame(dict(a=[1,3,2], b=[3,2,1]))
fig = df.plot.bar()
fig.show()

In [9]:

import pandas as pd
pd.options.plotting.backend = "plotly"

df = pd.DataFrame(dict(a=[1,3,2], b=[3,2,1]))
fig = df.plot.barh()
fig.show()

In [10]:

import pandas as pd
import numpy as np
pd.options.plotting.backend = "plotly"
np.random.seed(1)

df = pd.DataFrame(dict(
    a=np.random.normal(loc=1, scale=2, size=100),
    b=np.random.normal(loc=2, scale=1, size=100)
))
fig = df.plot.hist()
fig.show()

In [11]:

import pandas as pd
import numpy as np
pd.options.plotting.backend = "plotly"
np.random.seed(1)

df = pd.DataFrame(dict(
    a=np.random.normal(loc=1, scale=2, size=100),
    b=np.random.normal(loc=2, scale=1, size=100)
))
fig = df.plot.box()
fig.show()

The Pandas plotting API also exposes .hist() on DataFrames and Series objects, and .boxplot() on DataFrames, which can also be used with the Plotly backend.

In [12]:

import pandas as pd
import numpy as np
pd.options.plotting.backend = "plotly"
np.random.seed(1)

df = pd.DataFrame(dict(
    a=np.random.normal(loc=1, scale=2, size=100),
    b=np.random.normal(loc=2, scale=1, size=100)
))
fig = df.boxplot()
fig.show()

There also exists an independent third-party wrapper library around Plotly called Cufflinks, which provides similar functionality (with an API closer to that of Pandas' default matplotlib backend) by adding a .iplot() method to Pandas dataframes, as it was developed before Pandas supported configurable backends. Issues and questions regarding Cufflinks should be raised in the Cufflinks repository.

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter

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