How to make polar charts in Python with Plotly.
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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()
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()
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()
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()
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()
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()
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()
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(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter
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