Polar Charts in Python

How to make polar charts in Python with Plotly.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Polar chart with Plotly Express¶

A polar chart represents data along radial and angular axes. With Plotly Express, it is possible to represent polar data as scatter markers with px.scatter_polar, and as lines with px.line_polar.

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

For other types of arguments, see the section below using go.Scatterpolar.

The radial and angular coordinates are given with the r and theta arguments of px.scatter_polar. In the example below the theta data are categorical, but numerical data are possible too and the most common case.

In [1]:

import plotly.express as px
df = px.data.wind()
fig = px.scatter_polar(df, r="frequency", theta="direction")
fig.show()

The "strength" column corresponds to strength categories of the wind, and there is a frequency value for each direction and strength. Below we use the strength column to encode the color, symbol and size of the markers.

In [2]:

import plotly.express as px
df = px.data.wind()
fig = px.scatter_polar(df, r="frequency", theta="direction",
                       color="strength", symbol="strength", size="frequency",
                       color_discrete_sequence=px.colors.sequential.Plasma_r)
fig.show()

For a line polar plot, use px.line_polar:

In [3]:

import plotly.express as px
df = px.data.wind()
fig = px.line_polar(df, r="frequency", theta="direction", color="strength", line_close=True,
                    color_discrete_sequence=px.colors.sequential.Plasma_r,
                    template="plotly_dark",)
fig.show()

See also the wind rose page for more wind rose visualizations in polar coordinates.

You can plot less than a whole circle with the range_theta argument, and also control the start_angle and direction:

In [4]:

import plotly.express as px
fig = px.scatter_polar(r=range(0,90,10), theta=range(0,90,10),
                       range_theta=[0,90], start_angle=0, direction="counterclockwise")
fig.show()

Polar Scatter Plot with go.Scatterpolar¶

If Plotly Express does not provide a good starting point, you can use the more generic go.Scatterpolar class from plotly.graph_objects. All the options are documented in the reference page.

Basic Polar Chart¶

In [5]:

import plotly.graph_objects as go

fig = go.Figure(data=
    go.Scatterpolar(
        r = [0.5,1,2,2.5,3,4],
        theta = [35,70,120,155,205,240],
        mode = 'markers',
    ))

fig.update_layout(showlegend=False)
fig.show()

Line Polar Chart¶

In [6]:

import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/polar_dataset.csv")

fig = go.Figure()
fig.add_trace(go.Scatterpolar(
        r = df['x1'],
        theta = df['y'],
        mode = 'lines',
        name = 'Figure 8',
        line_color = 'peru'
    ))
fig.add_trace(go.Scatterpolar(
        r = df['x2'],
        theta = df['y'],
        mode = 'lines',
        name = 'Cardioid',
        line_color = 'darkviolet'
    ))
fig.add_trace(go.Scatterpolar(
        r = df['x3'],
        theta = df['y'],
        mode = 'lines',
        name = 'Hypercardioid',
        line_color = 'deepskyblue'
    ))


fig.update_layout(
    title = 'Mic Patterns',
    showlegend = False
)

fig.show()

Polar Bar Chart¶

a.k.a matplotlib logo in a few lines of code

In [7]:

import plotly.graph_objects as go

fig = go.Figure(go.Barpolar(
    r=[3.5, 1.5, 2.5, 4.5, 4.5, 4, 3],
    theta=[65, 15, 210, 110, 312.5, 180, 270],
    width=[20,15,10,20,15,30,15,],
    marker_color=["#E4FF87", '#709BFF', '#709BFF', '#FFAA70', '#FFAA70', '#FFDF70', '#B6FFB4'],
    marker_line_color="black",
    marker_line_width=2,
    opacity=0.8
))

fig.update_layout(
    template=None,
    polar = dict(
        radialaxis = dict(range=[0, 5], showticklabels=False, ticks=''),
        angularaxis = dict(showticklabels=False, ticks='')
    )
)

fig.show()

Categorical Polar Chart¶

In [8]:

import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(rows=2, cols=2, specs=[[{'type': 'polar'}]*2]*2)

fig.add_trace(go.Scatterpolar(
      name = "angular categories",
      r = [5, 4, 2, 4, 5],
      theta = ["a", "b", "c", "d", "a"],
    ), 1, 1)
fig.add_trace(go.Scatterpolar(
      name = "radial categories",
      r = ["a", "b", "c", "d", "b", "f", "a"],
      theta = [1, 4, 2, 1.5, 1.5, 6, 5],
      thetaunit = "radians",
    ), 1, 2)
fig.add_trace(go.Scatterpolar(
      name = "angular categories (w/ categoryarray)",
      r = [5, 4, 2, 4, 5],
      theta = ["a", "b", "c", "d", "a"],
    ), 2, 1)
fig.add_trace(go.Scatterpolar(
      name = "radial categories (w/ category descending)",
      r = ["a", "b", "c", "d", "b", "f", "a", "a"],
      theta = [45, 90, 180, 200, 300, 15, 20, 45],
    ), 2, 2)

fig.update_traces(fill='toself')
fig.update_layout(
    polar = dict(
      radialaxis_angle = -45,
      angularaxis = dict(
        direction = "clockwise",
        period = 6)
    ),
    polar2 = dict(
      radialaxis = dict(
        angle = 180,
        tickangle = -180 # so that tick labels are not upside down
      )
    ),
    polar3 = dict(
      sector = [80, 400],
      radialaxis_angle = -45,
      angularaxis_categoryarray = ["d", "a", "c", "b"]
    ),
    polar4 = dict(
      radialaxis_categoryorder = "category descending",
      angularaxis = dict(
        thetaunit = "radians",
        dtick = 0.3141592653589793
      ))
)

fig.show()

Polar Chart Sector¶

In [9]:

import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(rows=1, cols=2, specs=[[{'type': 'polar'}]*2])

fig.add_trace(go.Scatterpolar(), 1, 1)
fig.add_trace(go.Scatterpolar(), 1, 2)

# Same data for the two Scatterpolar plots, we will only change the sector in the layout
fig.update_traces(mode = "lines+markers",
      r = [1,2,3,4,5],
      theta = [0,90,180,360,0],
      line_color = "magenta",
      marker = dict(
        color = "royalblue",
        symbol = "square",
        size = 8
      ))

# The sector is [0, 360] by default, we update it for the first plot only
fig.update_layout(
    showlegend = False,
    polar = dict(# setting parameters for the second plot would be polar2=dict(...)
      sector = [150,210],
    ))


fig.show()

Polar Chart Directions¶

In [10]:

import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(rows=1, cols=2, specs=[[{'type': 'polar'},    {'type': 'polar'}]])

r = [1,2,3,4,5]
theta = [0,90,180,360,0]

fig.add_trace(go.Scatterpolar(), 1, 1)
fig.add_trace(go.Scatterpolar(), 1, 2)

# Same data for the two Scatterpolar plots, we will only change the direction in the layout
fig.update_traces(r= r, theta=theta,
                  mode="lines+markers", line_color='indianred',
                  marker=dict(color='lightslategray', size=8, symbol='square'))
fig.update_layout(
    showlegend = False,
    polar = dict(
      radialaxis_tickfont_size = 8,
      angularaxis = dict(
        tickfont_size=8,
        rotation=90, # start position of angular axis
        direction="counterclockwise"
      )
    ),
    polar2 = dict(
      radialaxis_tickfont_size = 8,
      angularaxis = dict(
        tickfont_size = 8,
        rotation = 90,
        direction = "clockwise"
      ),
    ))

fig.show()

Webgl Polar Chart¶

The go.Scatterpolargl trace uses the WebGL plotting engine for GPU-accelerated rendering.

In [11]:

import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/hobbs-pearson-trials.csv")

fig = go.Figure()

fig.add_trace(go.Scatterpolargl(
      r = df.trial_1_r,
      theta = df.trial_1_theta,
      name = "Trial 1",
      marker=dict(size=15, color="mediumseagreen")
    ))
fig.add_trace(go.Scatterpolargl(
      r = df.trial_2_r,
      theta = df.trial_2_theta,
      name = "Trial 2",
      marker=dict(size=20, color="darkorange")
    ))
fig.add_trace(go.Scatterpolargl(
      r = df.trial_3_r,
      theta = df.trial_3_theta,
      name = "Trial 3",
      marker=dict(size=12, color="mediumpurple")
    ))
fig.add_trace(go.Scatterpolargl(
      r = df.trial_4_r,
      theta = df.trial_4_theta,
      name = "Trial 4",
      marker=dict(size=22, color = "magenta")
    ))
fig.add_trace(go.Scatterpolargl(
      r = df.trial_5_r,
      theta = df.trial_5_theta,
      name = "Trial 5",
      marker=dict(size=19, color = "limegreen")
      ))
fig.add_trace(go.Scatterpolargl(
      r = df.trial_6_r,
      theta = df.trial_6_theta,
      name = "Trial 6",
      marker=dict(size=10, color = "gold")
      ))

# Common parameters for all traces
fig.update_traces(mode="markers", marker=dict(line_color='white', opacity=0.7))

fig.update_layout(
    title = "Hobbs-Pearson Trials",
    font_size = 15,
    showlegend = False,
    polar = dict(
      bgcolor = "rgb(223, 223, 223)",
      angularaxis = dict(
        linewidth = 3,
        showline=True,
        linecolor='black'
      ),
      radialaxis = dict(
        side = "counterclockwise",
        showline = True,
        linewidth = 2,
        gridcolor = "white",
        gridwidth = 2,
      )
    ),
    paper_bgcolor = "rgb(223, 223, 223)"
)

fig.show()

Polar Chart Subplots¶

In [12]:

import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(rows=2, cols=2, specs=[[{'type': 'polar'}]*2]*2)

fig.add_trace(go.Scatterpolar(
        r = [1, 2, 3],
        theta = [50, 100, 200],
        marker_symbol = "square"
    ), 1, 1)
fig.add_trace(go.Scatterpolar(
        r = [1, 2, 3],
        theta = [1, 2, 3],
        thetaunit = "radians"
    ), 1, 1)
fig.add_trace(go.Scatterpolar(
        r = ["a", "b", "c", "b"],
        theta = ["D", "C", "B", "A"],
        subplot = "polar2"
    ), 1, 2)
fig.add_trace(go.Scatterpolar(
        r = [50, 300, 900],
        theta = [0, 90, 180],
        subplot = "polar3"
    ), 2, 1)
fig.add_trace(go.Scatterpolar(
        mode = "lines",
        r = [3, 3, 4, 3],
        theta = [0, 45, 90, 270],
        fill = "toself",
        subplot = "polar4"
    ), 2, 2)


fig.update_layout(
    polar = dict(
      radialaxis_range = [1, 4],
      angularaxis_thetaunit = "radians"
    ),
    polar3 = dict(
      radialaxis = dict(type = "log", tickangle = 45),
      sector = [0, 180]
    ),
    polar4 = dict(
      radialaxis = dict(visible = False, range = [0, 6])),
    showlegend = False
)

fig.show()

Reference¶

See function reference for px.(scatter_polar) or function reference for px.(line_polar) or https://plotly.com/python/reference/scatterpolar/ for more information and chart attribute options!

What About Dash?¶

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_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter