Python:Matplotlib step()

step() creates a piecewise-constant (step) plot from 1-D data. Each sample in y is represented as a horizontal segment and adjacent samples are connected by vertical lines. The where parameter controls whether the step change happens before, after, or at the midpoint of the x-coordinate.

Syntax

matplotlib.pyplot.step(x, y, *args, where='pre', **kwargs)

Parameters:

• x (array-like): 1-D sequence of x positions. It is assumed (but not enforced) that x is increasing.
• y (array-like): Sequence of y-values. Must have the same length as x.
• *args: positional arguments forwarded to plot() (for example a format string such as 'o-').
• where ({ 'pre', 'post', 'mid' }, optional) — controls the placement of the steps:
  • 'pre' (default): the y value is constant to the left of each x position; the interval (x[i-1], x[i]] has value y[i].
  • 'post': the y value is constant to the right of each x position; the interval [x[i], x[i+1]) has value y[i].
  • 'mid': each step is centered between neighbors; level changes occur at midpoints between successive x values.
• **kwargs: any other keyword arguments accepted by matplotlib.pyplot.plot, such as label, linewidth, linestyle, alpha, etc.

Returns:

A list of matplotlib.lines.Line2D objects representing the plotted steps.

Notes:

• Use plt.stairs() if you have explicit step edges (left/right boundaries) rather than sample positions.
• step() is a thin wrapper around plot() and supports most plot formatting options.

Example

The example below draws three three-step series using where='pre', where='mid', and where='post'.

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 5, 6)
y = np.array([0, 1, 0, 1, 0, 1])

plt.figure(figsize=(6, 3))
plt.step(x, y + 2, where='pre', label="pre")
plt.step(x, y + 1, where='mid', label="mid")
plt.step(x, y + 0, where='post', label="post")

plt.ylim(-0.5, 3.5)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Example: matplotlib.pyplot.step')
plt.legend()
plt.grid(True)

plt.show()

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The output of this code will be: