How do I tell Matplotlib to create a second (new) plot, then later plot on the old one?

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? doe开发者_如何学Gos something similar, but not quite! It doesn't let me get access to that original plot.

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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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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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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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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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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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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()