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.

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.

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).

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!)

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()

I need to add two subplots to a figure. One subplot needs to be about three times as wide as the second (same height). I accomplished this using GridSpec and the colspan argument but I would like to do this using figure so I can save to PDF. I can adjust the first figure using the figsize argument in the constructor, but how do I change the size of the second plot?

Another way is to use the subplots function and pass the width ratio with gridspec_kw:

import numpy as np
import matplotlib.pyplot as plt 

# generate some data
x = np.arange(0, 10, 0.2)
y = np.sin(x)

# plot it
f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
a0.plot(x, y)
a1.plot(y, x)

f.tight_layout()
f.savefig('grid_figure.pdf')

You can use gridspec and figure:

import numpy as np
import matplotlib.pyplot as plt 
from matplotlib import gridspec

# generate some data
x = np.arange(0, 10, 0.2)
y = np.sin(x)

# plot it
fig = plt.figure(figsize=(8, 6)) 
gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1]) 
ax0 = plt.subplot(gs[0])
ax0.plot(x, y)
ax1 = plt.subplot(gs[1])
ax1.plot(y, x)

plt.tight_layout()
plt.savefig('grid_figure.pdf')

Probably the simplest way is using subplot2grid, described in Customizing Location of Subplot Using GridSpec.

ax = plt.subplot2grid((2, 2), (0, 0))

is equal to

import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(2, 2)
ax = plt.subplot(gs[0, 0])

so bmu’s example becomes:

import numpy as np
import matplotlib.pyplot as plt

# generate some data
x = np.arange(0, 10, 0.2)
y = np.sin(x)

# plot it
fig = plt.figure(figsize=(8, 6))
ax0 = plt.subplot2grid((1, 3), (0, 0), colspan=2)
ax0.plot(x, y)
ax1 = plt.subplot2grid((1, 3), (0, 2))
ax1.plot(y, x)

plt.tight_layout()
plt.savefig('grid_figure.pdf')

I used pyplot‘s axes object to manually adjust the sizes without using GridSpec:

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 10, 0.2)
y = np.sin(x)

# definitions for the axes
left, width = 0.07, 0.65
bottom, height = 0.1, .8
bottom_h = left_h = left+width+0.02

rect_cones = [left, bottom, width, height]
rect_box = [left_h, bottom, 0.17, height]

fig = plt.figure()

cones = plt.axes(rect_cones)
box = plt.axes(rect_box)

cones.plot(x, y)

box.plot(y, x)

plt.show()