Difference between plt.close() and plt.clf()

Question

In Python, what is the difference between plt.clf() and plt.close()?

Will they function the same way?

Answer 1

plt.close() will close the figure window entirely, where plt.clf() will just clear the figure - you can still paint another plot onto it.

It sounds like, for your needs, you should be preferring plt.clf(), or better yet keep a handle on the line objects themselves (they are returned in lists by plot calls) and use .set_data on those in subsequent iterations.

Answer 2

plt.clf() clears the entire current figure with all its axes, but leaves the window opened, such that it may be reused for other plots.

plt.close() closes a window, which will be the current window, if not specified otherwise.

Answer 3

There is a slight difference between the two functions.

plt.close() - It altogether plots the graph in seperate windows,releasing memory,retaining each window for view.

plt.clf() - We can say,it displays the graph in the same window one after other

For illustration, I have plotted two graphs with paramters year and views on X axis and Y axis each. Initially I have used closed function.it displayed the graphs in two seperate windows…

Afterwords, when I run the program with clf() it clears the graph and displays next one in same window i.e figure 1. Here is the code snippet -

    import matplotlib.pyplot as plt
    year = [2001,2002,2003,2004]
    Views= [12000,14000,16000,18000]
    Views2 = [15000,1800,24000,84000]
    plt.plot(year,Views)
    plt.show()
    plt.clf()
    plt.plot(year,Views2)
    plt.show()
    plt.clf()

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Answer 4

I think it is worth mentioning that plt.close() releases the memory, thus is preferred when generating and saving many figures in one run.

Using plt.clf() in such case will produce a warning after 20 plots (even if they are not going to be shown by plt.show()):

More than 20 figures have been opened. Figures created through the pyplot interface (matplotlib.pyplot.figure) are retained until explicitly closed and may consume too much memory.