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: