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:
