matplotlib.pyplot.semilogy() function in Python

Matplotlib is the most popular and Python-ready package that is used for visualizing the data. We use matplotlib for plotting high-quality charts, graphs, and figures.

matplotlib.pyplot.semilogy() Function

The matplotlib.pyplot.semilogy() function in pyplot module of matplotlib library is used to make a plot with log scaling on the y axis.

Syntax: matplotlib.pyplot.semilogy(*args, **kwargs)

Parameters: This method accept the following parameters that are described below:

• basey: This parameter is the base of the y logarithm and are optional with default value 10.
• subsy: This parameter is the sequence of location of the minor y ticks and is optional.
• nonposy: This parameter is a non-positive values in y that can be masked as invalid, or clipped to a very small positive number.

Returns: This returns the following:

• lines:This returns the list of Line2D objects representing the plotted data..

Below examples illustrate the matplotlib.pyplot.semilogy() function in matplotlib.pyplot:
Example #1:

# importing necessary libraries
import matplotlib.pyplot as plot
import numpy as np

# Year data for the semilogy plot
years = [1900, 1910, 1920, 1930, 1940, 1950,
         1960, 1970, 1980, 1990, 2000, 2010,
         2017]

# index data - taken at end of every
# decade - for the semilogy plot
indexValues = [68, 81, 71, 244, 151, 200, 615,
               809, 824, 2633, 10787, 11577,
               20656]

# Display grid
plot.grid(True, which ="both")

# Linear X axis, Logarithmic Y axis
plot.semilogy(years, indexValues )

plot.ylim([10, 21000])

plot.xlim([1900, 2020])

# Provide the title for the semilogy plot
plot.title('Y axis in Semilogy using Python Matplotlib')

# Give x axis label for the semilogy plot
plot.xlabel('Year')

# Give y axis label for the semilogy plot
plot.ylabel('Stock market index')

# Display the semilogy plot
plot.show()

Output:

Example #2:

# importing necessary libraries
import matplotlib.pyplot as plt
import numpy as np


fig, ax = plt.subplots(nrows = 2,
                      ncols = 2,
                      figsize =(10, 5))
x = np.random.randn(1000)

# Plot to each different index
ax[0, 0].loglog(x, x / 2);
ax[0, 1].semilogy(np.random.random(10), np.random.random(10));
ax[1, 0].semilogx(np.random.random(10), np.random.random(10));
ax[1, 1].hist(np.random.randn(1000));

Output:

Example #3:

# importing necessary libraries
import matplotlib.pyplot as plt
import numpy as np


x = [1, 2, 3, 4, 5]
y = [11, 22, 33, 44, 55]

fig, ax = plt.subplots()
ax.semilogy(x, y);

Output: