How to Create Matplotlib Plots with Log Scales

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Often you may want to create Matplotlib plots with log scales for one or more axes. Fortunately Matplotlib offers the following three functions for doing so:

• Matplotlib.pyplot.semilogx() – Make a plot with log scaling on the x-axis.
• Matplotlib.pyplot.semilogy() – Make a plot with log scaling on the y-axis.
• Matplotlib.pyplot.loglog() – Make a plot with log scaling on both axes. 

This tutorial explains how to use each of these functions in practice.

Example 1: Log Scale for the X-Axis

Suppose we create a line chart for the following data:

import matplotlib.pyplot as plt

#create data
x = [1, 8, 190, 1400, 6500] 
y = [1, 2, 3, 4, 5]

#create line chart of data
plt.plot(x,y)

We can use the .semilogx() function to convert the x-axis to a log scale:

plt.semilogx()

Note that the y-axis is the exact same, but the x-axis is now on a log scale.

Example 2: Log Scale for the Y-Axis

Suppose we create a line chart for the following data:

import matplotlib.pyplot as plt

#create data
x = [1, 2, 3, 4, 5]
y = [1, 8, 190, 1400, 6500]

#create line chart of data
plt.plot(x,y)

We can use the .semilogy() function to convert the y-axis to a log scale:

plt.semilogy()

Note that the x-axis is the exact same, but the y-axis is now on a log scale.

Example 3: Log Scale for Both Axes

Suppose we create a line chart for the following data:

import matplotlib.pyplot as plt

#create data
x = [10, 200, 3000, 40000, 500000]
y = [30, 400, 5000, 60000, 750000]

#create line chart of data
plt.plot(x,y)

We can use the .loglog() function to convert the y-axis to a log scale:

plt.loglog(x, y)

Note that both axes are now on a log scale.

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