216

I want to plot data, then create a new figure and plot data2, and finally come back to the original plot and plot data3, kinda like this:

import numpy as np
import matplotlib as plt

x = arange(5)
y = np.exp(5)
plt.figure()
plt.plot(x, y)

z = np.sin(x)
plt.figure()
plt.plot(x, z)

w = np.cos(x)
plt.figure("""first figure""") # Here's the part I need
plt.plot(x, w)

FYI How do I tell matplotlib that I am done with a plot? does something similar, but not quite! It doesn't let me get access to that original plot.

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6 Answers 6

Reset to default
197

If you find yourself doing things like this regularly it may be worth investigating the object-oriented interface to matplotlib. In your case:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(5)
y = np.exp(x)
fig1, ax1 = plt.subplots()
ax1.plot(x, y)
ax1.set_title("Axis 1 title")
ax1.set_xlabel("X-label for axis 1")

z = np.sin(x)
fig2, (ax2, ax3) = plt.subplots(nrows=2, ncols=1) # two axes on figure
ax2.plot(x, z)
ax3.plot(x, -z)

w = np.cos(x)
ax1.plot(x, w) # can continue plotting on the first axis

It is a little more verbose but it's much clearer and easier to keep track of, especially with several figures each with multiple subplots.

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6 Comments

I prefer the object oriented approach as when I anticipate having many figures, it will be easier to keep track of them by using names rather than numbers. Thanks!
But how can one change the label and axis limits with this approach. If I use ax1.ylabel it says that it is not found. Same with fig1.ylabel ...
@GeorgeDatseris The syntax is a little different. It's ax1.set_xlabel("your x label"), ax1.set_ylabel("your y label"), and ax1.set_title("your title").
why did you use 111 ?
@yashSodha - that's a matlab-style specification of number of subplots (rows, col, index). But it's much easier now to use plt.subplots(nrows, ncols). Have updated the example.
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188

When you call figure, simply number the plot.

x = arange(5)
y = np.exp(5)
plt.figure(0)
plt.plot(x, y)

z = np.sin(x)
plt.figure(1)
plt.plot(x, z)

w = np.cos(x)
plt.figure(0) # Here's the part I need
plt.plot(x, w)

Edit: Note that you can number the plots however you want (here, starting from 0) but if you don't provide figure with a number at all when you create a new one, the automatic numbering will start at 1 ("Matlab Style" according to the docs).

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4 Comments

This appears to work in matplotlib's interactive mode, while the figure() ... add_subplot() method does not. Thanks!
@SebMa Please don't change the code without understanding it. This answer was specifically about passing a number to figure, which you removed. The other things you changed were copied from the original post and not mistakes in my answer.
@agf Hi, I removed the 1 inside plt.figure(1) because I thought the number was auto incremented and therefore not necessary. Sorry.
But then only figure(0) gets the title and legend
25

However, numbering starts at 1, so:

x = arange(5)
y = np.exp(5)
plt.figure(1)
plt.plot(x, y)

z = np.sin(x)
plt.figure(2)
plt.plot(x, z)

w = np.cos(x)
plt.figure(1) # Here's the part I need, but numbering starts at 1!
plt.plot(x, w)

Also, if you have multiple axes on a figure, such as subplots, use the axes(h) command where h is the handle of the desired axes object to focus on that axes.

(don't have comment privileges yet, sorry for new answer!)

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1 Comment

0 works, automatic numbering just start at 1, if you don't give it a number at all.
5

The accepted answer here says to use the object oriented interface (matplotlib) but the answer itself incoporates some of the MATLAB-style interface (matplotib.pyplot).

It is possible to use solely the OOP method, if you like that sort of thing:

import numpy as np
import matplotlib

x = np.arange(5)
y = np.exp(x)
first_figure      = matplotlib.figure.Figure()
first_figure_axis = first_figure.add_subplot()
first_figure_axis.plot(x, y)

z = np.sin(x)
second_figure      = matplotlib.figure.Figure()
second_figure_axis = second_figure.add_subplot()
second_figure_axis.plot(x, z)

w = np.cos(x)
first_figure_axis.plot(x, w)

display(first_figure) # Jupyter
display(second_figure)

This gives the user manual control over the figures, and avoids problems associated with pyplot's internal state supporting only a single figure.

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Comments

2

An easy way to plot separate frame for each iteration could be:

import matplotlib.pyplot as plt  
for grp in list_groups:
        plt.figure()
        plt.plot(grp)
        plt.show()

Then python will plot different frames.

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1 Comment

Seems to be exactly what the OP asked for, and what is required when using the original interface.
1

One way I found after some struggling is creating a function which gets data_plot matrix, file name and order as parameter to create boxplots from the given data in the ordered figure (different orders = different figures) and save it under the given file_name.

def plotFigure(data_plot,file_name,order):
    fig = plt.figure(order, figsize=(9, 6))
    ax = fig.add_subplot(111)
    bp = ax.boxplot(data_plot)
    fig.savefig(file_name, bbox_inches='tight')
    plt.close()
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