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pyplot.show() The relevant function is pyplot.yscale(). If you use the object-oriented version, replace it by the method Axes.set_yscale(). Remember that you can also change the scale of X axis, using pyplot.xscale() (or Axes.set_xscale()).
1 Ιαν 2021 · Syntax: matplotlib.pyplot.yscale (value, **kwargs) Parameters: value = { “linear”, “log”, “symlog”, “logit”, …. **kwargs = Different keyword arguments are accepted, depending on the scale (matplotlib.scale.LinearScale, LogScale, SymmetricalLogScale, LogitScale) Returns : Converts the y-axes to the given scale type.
Make a plot with log scaling on both the x- and y-axis. Call signatures: loglog([x], y, [fmt], data=None, **kwargs) loglog([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) This is just a thin wrapper around plot which additionally changes both the x-axis and the y-axis to log scaling.
How to Plot Logarithmic Axes in Matplotlib is an essential skill for data visualization in Python. Logarithmic axes are particularly useful when dealing with data that spans several orders of magnitude or when you want to emphasize relative changes rather than absolute differences.
Examples of plots with logarithmic axes. import matplotlib.pyplot as plt import numpy as np # Data for plotting t = np.arange(0.01, 20.0, 0.01) # Create figure fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) # log y axis ax1.semilogy(t, np.exp(-t / 5.0)) ax1.set(title='semilogy') ax1.grid() # log x axis ax2.semilogx(t, np.sin(2 * np.pi * t ...
11 Φεβ 2022 · If you are using the object-oriented interface in matplotlib you can use matplotlib.axes.Axes.set_xscale('log') or matplotlib.axes.Axes.set_yscale('log') for X or Y axis respectively. import matplotlib.pyplot as plt data = [pow(10, i) for i in range(10)] fig, ax = plt.subplots() ax.plot(data) ax.set_yscale('log') plt.show()
Detailed examples of Log Plots including changing color, size, log axes, and more in Python.
