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

## Question:

This should be easy but I have just started toying with matplotlib and python. I can do a line or a scatter plot but i am not sure how to do a simple step function. Any help is much appreciated.

``````x = 1,2,3,4
y = 0.002871972681775004, 0.00514787917410944, 0.00863476098280219, 0.012003316194034325
``````

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.

It seems like you want `step`.

E.g.

``````import matplotlib.pyplot as plt

x = [1,2,3,4]
y = [0.002871972681775004, 0.00514787917410944,
0.00863476098280219, 0.012003316194034325]

plt.step(x, y)
plt.show()
``````

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)
``````

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