Log Plots in Python

How to make Log plots in Python with Plotly.

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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.

This page shows examples of how to configure 2-dimensional Cartesian axes to follow a logarithmic rather than linear progression. Configuring gridlines, ticks, tick labels and axis titles on logarithmic axes is done the same was as with linear axes.

Logarithmic Axes with Plotly Express¶

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.

All of Plotly Express' 2-D Cartesian functions include the log_x and log_y keyword arguments, which can be set to True to set the corresponding axis to a logarithmic scale:

In [1]:

import plotly.express as px
df = px.data.gapminder().query("year == 2007")

fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country", log_x=True)
fig.show()

Setting the range of a logarithmic axis with Plotly Express works the same was as with linear axes: using the range_x and range_y keywords. Note that you cannot set the range to include 0 or less.

In [2]:

import plotly.express as px
df = px.data.gapminder().query("year == 2007")

fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country",
                 log_x=True, range_x=[1,100000], range_y=[0,100])
fig.show()

Adding minor ticks¶

new in 5.8

You can position and style minor ticks using minor. This takes a dict of properties to apply to minor ticks. See the figure reference for full details on the accepted keys in this dict.

In this example we set the tick length with ticklen, add the ticks on the inside with ticks="inside", and turn grid lines on with howgrid=True.

In [3]:

import plotly.express as px
df = px.data.gapminder().query("year == 2007")

fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country",
                 log_x=True, range_x=[1,100000], range_y=[0,100])

fig.update_xaxes(minor=dict(ticks="inside", ticklen=6, showgrid=True))

fig.show()

Logarithmic Axes with Graph Objects¶

If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Figure class from plotly.graph_objects.

In [4]:

import plotly.graph_objects as go
import plotly.express as px
df = px.data.gapminder().query("year == 2007")

fig = go.Figure()

fig.add_trace(go.Scatter(mode="markers", x=df["gdpPercap"], y=df["lifeExp"] ))

fig.update_xaxes(type="log")
fig.show()

Setting the range of a logarithmic axis with plotly.graph_objects is very different than setting the range of linear axes: the range is set using the exponent rather than the actual value:

In [5]:

import plotly.graph_objects as go
import plotly.express as px
df = px.data.gapminder().query("year == 2007")

fig = go.Figure()

fig.add_trace(go.Scatter(mode="markers", x=df["gdpPercap"], y=df["lifeExp"] ))

fig.update_xaxes(type="log", range=[0,5]) # log range: 10^0=1, 10^5=100000
fig.update_yaxes(range=[0,100]) # linear range
fig.show()

Reference¶

See function reference for px.(scatter) or https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-type 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