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There are a few methods given on this page (semilogx, semilogy, loglog) but they all do the same thing under the hood, which is to call set_xscale('log') (for x-axis) and set_yscale('log') (for y-axis).
10 Δεκ 2019 · To make a semi-log plot with x-scale logarithmic, there are two options: import matplotlib.pyplot as plt fig, ax = plt.subplots() ax.plot(x,y) ax.set_xscale('log') or
Axis scales# By default Matplotlib displays data on the axis using a linear scale. Matplotlib also supports logarithmic scales, and other less common scales as well. Usually this can be done directly by using the set_xscale or set_yscale methods.
1 Ιαν 2021 · Syntax : matplotlib.pyplot.xscale (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 x-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.
In this example, we use plt.loglog () to create a plot with both x and y axes on a logarithmic scale. The np.logspace () function generates logarithmically spaced numbers, which is ideal for demonstrating how to plot logarithmic axes in Matplotlib.
Detailed examples of Log Plots including changing color, size, log axes, and more in Python.
