How to put the y-axis in logarithmic scale with Matplotlib ?

To transform an axis in logarithmic scale with Matplotlib, a solution is to use the pyplot functions xscale and yscale:

Example 1

Let's take for example the exponential function:

import matplotlib.pyplot as plt
import numpy as np

x_min = 0
x_max = 10.0

x = np.arange(x_min, x_max, .01)
y = np.exp(x)

plt.plot(x,y)

plt.xlim(x_min,x_max)
plt.ylim(np.exp(x_min),np.exp(x_max))

plt.grid(True,which="both", linestyle='--')

plt.title('How to add a grid on a figure in matplotlib ?', fontsize=8)

plt.savefig("matplotlib_grid_03.png", bbox_inches='tight')
plt.close()

To change in logarithmic scale the y-axis, we can add: plt.yscale('log')

import matplotlib.pyplot as plt
import numpy as np

x_min = 0
x_max = 10.0

x = np.arange(x_min, x_max, .01)
y = np.exp(x)

plt.plot(x,y)

plt.xlim(x_min,x_max)
plt.ylim(np.exp(x_min),np.exp(x_max))

plt.yscale('log')

plt.grid(True,which="both", linestyle='--')

plt.title('How to add a grid on a figure in matplotlib ?', fontsize=8)

plt.savefig("matplotlib_grid_04.png", bbox_inches='tight')

Example 2

Another example with a Mie phase function (output_mie_code.txt)

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt

c1,c2,c3,c4,c5  = np.loadtxt("output_mie_code.txt", skiprows=2, unpack=True)

fig = plt.figure()
ax = fig.add_subplot(111)

plt.plot(c1,c2,'k--')
plt.yscale('log')

plt.grid(True,which="both")

plt.xlabel(r"Scattering Angle $\Theta$ ($^\circ$)")
plt.ylabel(r"$P_{11}$")

plt.show()

Note: To have the figure grid in logarithmic scale, just add the command plt.grid(True,which="both").

References