Log Scale in Matplotlib

Matplotlib is a versatile library for creating visualizations in Python. One commonly used feature in Matplotlib is the ability to use a logarithmic scale for the axes. This can be particularly useful when working with data that spans several orders of magnitude. In this article, we will explore how to use log scale in Matplotlib.

1. Plotting a Simple Line Chart with Log Scale

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.exp(x)

plt.figure()
plt.plot(x, y)
plt.yscale('log')
plt.show()

Output:

2. Changing the Base of Log Scale

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.exp(x)

plt.figure()
plt.plot(x, y)
plt.yscale('log', base=2)
plt.show()

Output:

3. Dual Axes with Different Scales

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.exp(x)

fig, ax1 = plt.subplots()

ax1.plot(x, y, 'b-')
ax1.set_yscale('log')

ax2 = ax1.twinx()
ax2.plot(x, y**2, 'r-')
ax2.set_yscale('linear')

plt.show()

Output:

4. Log Scale for both X and Y Axis

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.exp(x)

plt.figure()
plt.plot(x, y)
plt.xscale('log')
plt.yscale('log')
plt.show()

Output:

5. Adding Gridlines to Log-Scaled Plot

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.exp(x)

plt.figure()
plt.plot(x, y)
plt.yscale('log')
plt.grid(True, which="both", ls="--")
plt.show()

Output:

6. Using Logarithmic Ticks

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.exp(x)

plt.figure()
plt.plot(x, y)
plt.yscale('log')
plt.minorticks_on()
plt.grid(True, which="both", ls="--")
plt.show()

Output:

7. Log Scale Histogram

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.exp(x)

data = np.random.exponential(1, 1000)

plt.figure()
plt.hist(data, bins=50)
plt.yscale('log')
plt.show()

Output:

8. Log Scale with Error Bars

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(1, 11)
y = np.exp(x)
error = 0.1 * y 

plt.figure()
plt.errorbar(x, y, yerr=error)
plt.yscale('log')
plt.show()

Output:

9. Log Scale Heatmap

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.exp(x)

data = np.random.rand(10,10)

plt.figure()
plt.imshow(data, norm=LogNorm())
plt.colorbar()
plt.show()

10. Contour Plot with Log Scale

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 10, 100)
y = np.linspace(1, 10, 100)
X, Y = np.meshgrid(x, y)
Z = np.exp(X + Y)

plt.figure()
plt.contourf(X, Y, Z)
plt.yscale('log')
plt.colorbar()
plt.show()

Output:

Conclusion

In this article, we have explored various ways to use log scale in Matplotlib. By utilizing log scale, we can effectively visualize data that spans a wide range of values. Whether it is a simple line chart or a complex contour plot, Matplotlib provides the flexibility to create log-scaled plots for a wide range of applications. Next time you are working with data that has large variations, consider incorporating log scale into your visualizations using Matplotlib.