matplotlib Axes.plot() vs pyplot.plot()

Question

What is the difference between the Axes.plot() and pyplot.plot() methods? Does one use another as a subroutine?

It seems that my options for plotting are

line = plt.plot(data)

or

ax = plt.axes()
line = ax.plot(data)

or even

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
line = ax.plot(data)

Are there situations where it is preferable to use one over the other?

Answer 1

For drawing a single plot, the best practice is probably

fig = plt.figure()
plt.plot(data)
fig.show()

Now, lets take a look in to 3 examples from the question and explain what they do.

1. Takes the current figure and axes (if none exists it will create a new one) and plot into them.

   line = plt.plot(data)
   

2. In your case, the behavior is same as before with explicitly stating the axes for plot.

    ax = plt.axes()
    line = ax.plot(data)
   

This approach of using ax.plot(...) is a must, if you want to plot into multiple axes (possibly in one figure). For example when using a subplots.

3. Explicitly creates new figure - you will not add anything to previous one. Explicitly creates a new axes with given rectangle shape and the rest is the same as with 2.

    fig = plt.figure()
    ax = fig.add_axes([0,0,1,1])
    line = ax.plot(data)
   

possible problem using figure.add_axes is that it may add a new axes object to the figure, which will overlay the first one (or others). This happens if the requested size does not match the existing ones.

Answer 2

There is essentially no difference. plt.plot will at some point (after making sure that there is a figure and an axes available to plot to) call the plot function from that axes instance.

So the main difference is rather at the user's side:

• do you want to use the Matlab-like state machine approach, which may save some lines of code for simple plotting tasks? Then use pyplot.
• do you want to have full control over the plotting using the more pythonic object oriented approach? Then use objects like axes explicitely.

You may want to read the matplotlib usage guide.

Answer 3

Pyplot's plotting methods can be applied to either the Pyplot root (pyplot.plot()) or an axes object (axes.plot()).

Calling a plotting function directly on the Pyplot library (pyplot.plot()) creates a default subplot (figure and axes). Calling it on an axes object (axes.plot()) requires that you to have created your own axes object already and puts the graph onto that customized plotting space.

While pyplot.plot() is easy to use, you have more control over your space (and better able to understand interaction with other libraries) if you create an axes object axes.plot().

Axes.plot() returns an axes object. Every axes object has a parent figure object. The axes object contains the methods for plotting, as well as most customization options, while the figure object stores all of the figure-level attributes and allow the plot to output as an image.

If you use pyplot.plot() method and want to start customizing your axes, you can find out the name of the default axes object it created by calling pyplot.gca() to "get current axes."

Answer 4

python plt.plot(): it will create many default subplots, will save many lines of code and is easy to understand.

Axes.plot(): using an axes object will give you a better ability to customize your plot space.

Answer 5

If this is still relevant, Matplotlib's official website has a clear answer on this issue. Go to "The object-oriented interface and pyplot interface".

This section clearly answers the question. Using 'fig, ax', i.e., object-oriented approach gives one more control for customizing our plot. Using 'pyplot', on the other hand leaves us with less control over our plot but the advantage is that it saves us from writing more lines of code and is easier and handy when dealing with single plot.

Official documentation of Matplotlib suggests that "which approach to use is solely an individual's choice and there is no preference of one over other. However, it is good to stick to one approach to maintain consistency."