How do I plot a step function with Matplotlib in Python?

Draw two lines, one at y=0, and one at y=1, cutting off at whatever x your step function is for.

e.g. if you want to step from 0 to 1 at x=2.3 and plot from x=0 to x=5:

import matplotlib.pyplot as plt
#                                 _
# if you want the vertical line _|
plt.plot([0,2.3,2.3,5],[0,0,1,1])
#
# OR:
#                                       _
# if you don't want the vertical line _
#plt.plot([0,2.3],[0,0],[2.3,5],[1,1])

# now change the y axis so we can actually see the line
plt.ylim(-0.1,1.1)

plt.show()

I think you want pylab.bar(x,y,width=1) or equally pyplot‘s bar method. if not checkout the gallery for the many styles of plots you can do. Each image comes with example code showing you how to make it using matplotlib.

If you have non-uniformly spaced data points, you can use the drawstyle keyword argument for plot:

x = [1,2.5,3.5,4] 
y = [0.002871972681775004, 0.00514787917410944, 
     0.00863476098280219, 0.012003316194034325]

plt.plot(x, y, drawstyle='steps-pre')

Also available are steps-mid and steps-post.

In case someone just wants to stepify some data rather than actually plot it:

def get_x_y_steps(x, y, where="post"):
    if where == "post":
        x_step = [x[0]] + [_x for tup in zip(x, x)[1:] for _x in tup]
        y_step = [_y for tup in zip(y, y)[:-1] for _y in tup] + [y[-1]]
    elif where == "pre":
        x_step = [_x for tup in zip(x, x)[:-1] for _x in tup] + [x[-1]]
        y_step = [y[0]] + [_y for tup in zip(y, y)[1:] for _y in tup]
    return x_step, y_step

New in matplotlib 3.4.0

There is a new plt.stairs method to complement plt.step:

plt.stairs and the underlying StepPatch provide a cleaner interface for plotting stepwise constant functions for the common case that you know the step edges.

This supersedes many use cases of plt.step, for instance when plotting the output of np.histogram.

Check out the official matplotlib gallery for how to use plt.stairs and StepPatch.

When to use plt.step vs plt.stairs

• Use the original plt.step if you have reference points. Here the steps are anchored at [1,2,3,4] and extended to the left:

  plt.step(x=[1,2,3,4], y=[20,40,60,30])
  

• Use the new plt.stairs if you have edges. The previous [1,2,3,4] step points correspond to [1,1,2,3,4] stair edges:

  plt.stairs(values=[20,40,60,30], edges=[1,1,2,3,4])
  

Using plt.stairs with np.histogram

Since np.histogram returns edges, it works directly with plt.stairs:

data = np.random.normal(5, 3, 3000)
bins = np.linspace(0, 10, 20)
hist, edges = np.histogram(data, bins)

plt.stairs(hist, edges)