Develop Reference

matplotlib, show only positive numbers starting from 0 on both sides on the X axis

I want to draw a plot with matplotlib with cylindrical symmetry. it means on both sides on X-axes positive (absolute values) numbers similar to this plot.
Plot that I get, but numbers on the left side of X-axes negative (that need to be positive too):

You are trying to change the format of the matplotlib.xaxis.Tick object label. In matplotlib there are usually a lot of ways to do things and this is no exception.
Matplotlib figures out where the ticks should be with a tick locator. Then a tick formatter defines what kind of text should be in those tick labels. The default formatter for numerical data is ScalarFormatter.
An alternative locator or formatter is easy to define with the matplotlib ticker library. Once you get the hang of this, its actually quite easy. Here is an example that shows how to fix your problem.
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ticker
# define fig and axes
fig, axes = plt.subplots(ncols=1, nrows=2, figsize=(17.5,6))
axes = axes.flatten() # make axes a 1D array
x = np.linspace(-20,20) # make some x values between -20 and 20
y = np.sin(x/4) # make some y values that are sin(x/4)
for ax in axes:
ax.plot(x, y, linewidth=2.5) # plot x and y values
#Make a function that makes negative values positive (lots of ways to do this)
def neg_to_pos(n, position):
n = int(n)
if n < 0:
return str(n * -1)
else:
return str(n)
#access the bottom plot object xaxis object and set the
#major tick formatter to be a FunctionFormatter that uses
#the neg_to_pos function
xaxis_formatter = axes[1].xaxis.set_major_formatter(ticker.FuncFormatter(neg_to_pos))
#add titles, x labels, and y labels
title_ax0 = axes[0].set_title('Default formatter', fontsize=14, fontweight='bold')
title_ax1 = axes[1].set_title('FuncFormatter', fontsize=14, fontweight='bold')
xlabels = [ax.set_xlabel('x_values') for ax in axes]
ylabels = [ax.set_ylabel('y_values') for ax in axes]
plt.subplots_adjust(hspace = 0.5) #fix the vertical spacing between the plots
The links I placed above for locators and formatters are bookmarked for me and I refer to them often when working on axis tick problems.

Error when trying to Move Figure Legend in MatplotLib [duplicate]

I have a series of 20 plots (not subplots) to be made in a single figure. I want the legend to be outside of the box. At the same time, I do not want to change the axes, as the size of the figure gets reduced.
I want to keep the legend box outside the plot area (I want the legend to be outside at the right side of the plot area).
Is there a way to reduce the font size of the text inside the legend box, so that the size of the legend box will be small?

There are a number of ways to do what you want. To add to what Christian Alis and Navi already said, you can use the bbox_to_anchor keyword argument to place the legend partially outside the axes and/or decrease the font size.
Before you consider decreasing the font size (which can make things awfully hard to read), try playing around with placing the legend in different places:
So, let's start with a generic example:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
fig = plt.figure()
ax = plt.subplot(111)
for i in xrange(5):
ax.plot(x, i * x, label='$y = %ix$' % i)
ax.legend()
plt.show()
If we do the same thing, but use the bbox_to_anchor keyword argument we can shift the legend slightly outside the axes boundaries:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
fig = plt.figure()
ax = plt.subplot(111)
for i in xrange(5):
ax.plot(x, i * x, label='$y = %ix$' % i)
ax.legend(bbox_to_anchor=(1.1, 1.05))
plt.show()
Similarly, make the legend more horizontal and/or put it at the top of the figure (I'm also turning on rounded corners and a simple drop shadow):
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
fig = plt.figure()
ax = plt.subplot(111)
for i in xrange(5):
line, = ax.plot(x, i * x, label='$y = %ix$'%i)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),
ncol=3, fancybox=True, shadow=True)
plt.show()
Alternatively, shrink the current plot's width, and put the legend entirely outside the axis of the figure (note: if you use tight_layout(), then leave out ax.set_position():
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
fig = plt.figure()
ax = plt.subplot(111)
for i in xrange(5):
ax.plot(x, i * x, label='$y = %ix$'%i)
# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
And in a similar manner, shrink the plot vertically, and put a horizontal legend at the bottom:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
fig = plt.figure()
ax = plt.subplot(111)
for i in xrange(5):
line, = ax.plot(x, i * x, label='$y = %ix$'%i)
# Shrink current axis's height by 10% on the bottom
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1,
box.width, box.height * 0.9])
# Put a legend below current axis
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
fancybox=True, shadow=True, ncol=5)
plt.show()
Have a look at the matplotlib legend guide. You might also take a look at plt.figlegend().

Placing the legend (bbox_to_anchor)
A legend is positioned inside the bounding box of the axes using the loc argument to plt.legend.
E.g., loc="upper right" places the legend in the upper right corner of the bounding box, which by default extents from (0, 0) to (1, 1) in axes coordinates (or in bounding box notation (x0, y0, width, height) = (0, 0, 1, 1)).
To place the legend outside of the axes bounding box, one may specify a tuple (x0, y0) of axes coordinates of the lower left corner of the legend.
plt.legend(loc=(1.04, 0))
A more versatile approach is to manually specify the bounding box into which the legend should be placed, using the bbox_to_anchor argument. One can restrict oneself to supply only the (x0, y0) part of the bbox. This creates a zero span box, out of which the legend will expand in the direction given by the loc argument. E.g.,
plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
places the legend outside the axes, such that the upper left corner of the legend is at position (1.04, 1) in axes coordinates.
Further examples are given below, where additionally the interplay between different arguments like mode and ncols are shown.
l1 = plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
l2 = plt.legend(bbox_to_anchor=(1.04, 0), loc="lower left", borderaxespad=0)
l3 = plt.legend(bbox_to_anchor=(1.04, 0.5), loc="center left", borderaxespad=0)
l4 = plt.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower left",
mode="expand", borderaxespad=0, ncol=3)
l5 = plt.legend(bbox_to_anchor=(1, 0), loc="lower right",
bbox_transform=fig.transFigure, ncol=3)
l6 = plt.legend(bbox_to_anchor=(0.4, 0.8), loc="upper right")
Details about how to interpret the 4-tuple argument to bbox_to_anchor, as in l4, can be found in this question. The mode="expand" expands the legend horizontally inside the bounding box given by the 4-tuple. For a vertically expanded legend, see this question.
Sometimes it may be useful to specify the bounding box in figure coordinates instead of axes coordinates. This is shown in the example l5 from above, where the bbox_transform argument is used to put the legend in the lower left corner of the figure.
Postprocessing
Having placed the legend outside the axes often leads to the undesired situation that it is completely or partially outside the figure canvas.
Solutions to this problem are:
Adjust the subplot parameters
One can adjust the subplot parameters such, that the axes take less space inside the figure (and thereby leave more space to the legend) by using plt.subplots_adjust. E.g.,
plt.subplots_adjust(right=0.7)
leaves 30% space on the right-hand side of the figure, where one could place the legend.
Tight layout
Using plt.tight_layout Allows to automatically adjust the subplot parameters such that the elements in the figure sit tight against the figure edges. Unfortunately, the legend is not taken into account in this automatism, but we can supply a rectangle box that the whole subplots area (including labels) will fit into.
plt.tight_layout(rect=[0, 0, 0.75, 1])
Saving the figure with bbox_inches = "tight"
The argument bbox_inches = "tight" to plt.savefig can be used to save the figure such that all artist on the canvas (including the legend) are fit into the saved area. If needed, the figure size is automatically adjusted.
plt.savefig("output.png", bbox_inches="tight")
Automatically adjusting the subplot parameters
A way to automatically adjust the subplot position such that the legend fits inside the canvas without changing the figure size can be found in this answer: Creating figure with exact size and no padding (and legend outside the axes)
Comparison between the cases discussed above:
Alternatives
A figure legend
One may use a legend to the figure instead of the axes, matplotlib.figure.Figure.legend. This has become especially useful for Matplotlib version 2.1 or later, where no special arguments are needed
fig.legend(loc=7)
to create a legend for all artists in the different axes of the figure. The legend is placed using the loc argument, similar to how it is placed inside an axes, but in reference to the whole figure - hence it will be outside the axes somewhat automatically. What remains is to adjust the subplots such that there is no overlap between the legend and the axes. Here the point "Adjust the subplot parameters" from above will be helpful. An example:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi)
colors = ["#7aa0c4", "#ca82e1", "#8bcd50", "#e18882"]
fig, axes = plt.subplots(ncols=2)
for i in range(4):
axes[i//2].plot(x, np.sin(x+i), color=colors[i], label="y=sin(x + {})".format(i))
fig.legend(loc=7)
fig.tight_layout()
fig.subplots_adjust(right=0.75)
plt.show()
Legend inside dedicated subplot axes
An alternative to using bbox_to_anchor would be to place the legend in its dedicated subplot axes (lax).
Since the legend subplot should be smaller than the plot, we may use gridspec_kw={"width_ratios":[4, 1]} at axes creation.
We can hide the axes lax.axis("off"), but we still put a legend in. The legend handles and labels need to obtained from the real plot via h, l = ax.get_legend_handles_labels() and can then be supplied to the legend in the lax subplot, lax.legend(h, l). A complete example is below.
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = 6, 2
fig, (ax, lax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios":[4, 1]})
ax.plot(x, y, label="y=sin(x)")
....
h, l = ax.get_legend_handles_labels()
lax.legend(h, l, borderaxespad=0)
lax.axis("off")
plt.tight_layout()
plt.show()
This produces a plot which is visually pretty similar to the plot from above:
We could also use the first axes to place the legend, but use the bbox_transform of the legend axes,
ax.legend(bbox_to_anchor=(0, 0, 1, 1), bbox_transform=lax.transAxes)
lax.axis("off")
In this approach, we do not need to obtain the legend handles externally, but we need to specify the bbox_to_anchor argument.
Further reading and notes:
Consider the Matplotlib legend guide with some examples of other stuff you want to do with legends.
Some example code for placing legends for pie charts may directly be found in answer to this question: Python - Legend overlaps with the pie chart
The loc argument can take numbers instead of strings, which make calls shorter, however, they are not very intuitively mapped to each other. Here is the mapping for reference:

Just call legend() after the plot() call like this:
# Matplotlib
plt.plot(...)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
# Pandas
df.myCol.plot().legend(loc='center left', bbox_to_anchor=(1, 0.5))
Results would look something like this:

You can make the legend text smaller by specifying set_size of FontProperties.
Resources:
Legend guide
matplotlib.legend
matplotlib.pyplot.legend
matplotlib.font_manager
set_size(self, size)
Valid font size are xx-small, x-small, small, medium, large, x-large, xx-large, larger, smaller, and None.
Real Python: Python Plotting With Matplotlib (Guide)
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
fontP = FontProperties()
fontP.set_size('xx-small')
p1, = plt.plot([1, 2, 3], label='Line 1')
p2, = plt.plot([3, 2, 1], label='Line 2')
plt.legend(handles=[p1, p2], title='title', bbox_to_anchor=(1.05, 1), loc='upper left', prop=fontP)
fontsize='xx-small' also works, without importing FontProperties.
plt.legend(handles=[p1, p2], title='title', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize='xx-small')

To place the legend outside the plot area, use loc and bbox_to_anchor keywords of legend(). For example, the following code will place the legend to the right of the plot area:
legend(loc="upper left", bbox_to_anchor=(1,1))
For more info, see the legend guide

Short answer: you can use bbox_to_anchor + bbox_extra_artists + bbox_inches='tight'.
Longer answer:
You can use bbox_to_anchor to manually specify the location of the legend box, as some other people have pointed out in the answers.
However, the usual issue is that the legend box is cropped, e.g.:
import matplotlib.pyplot as plt
# data
all_x = [10,20,30]
all_y = [[1,3], [1.5,2.9],[3,2]]
# Plot
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(all_x, all_y)
# Add legend, title and axis labels
lgd = ax.legend( [ 'Lag ' + str(lag) for lag in all_x], loc='center right', bbox_to_anchor=(1.3, 0.5))
ax.set_title('Title')
ax.set_xlabel('x label')
ax.set_ylabel('y label')
fig.savefig('image_output.png', dpi=300, format='png')
In order to prevent the legend box from getting cropped, when you save the figure you can use the parameters bbox_extra_artists and bbox_inches to ask savefig to include cropped elements in the saved image:
fig.savefig('image_output.png', bbox_extra_artists=(lgd,), bbox_inches='tight')
Example (I only changed the last line to add 2 parameters to fig.savefig()):
import matplotlib.pyplot as plt
# data
all_x = [10,20,30]
all_y = [[1,3], [1.5,2.9],[3,2]]
# Plot
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(all_x, all_y)
# Add legend, title and axis labels
lgd = ax.legend( [ 'Lag ' + str(lag) for lag in all_x], loc='center right', bbox_to_anchor=(1.3, 0.5))
ax.set_title('Title')
ax.set_xlabel('x label')
ax.set_ylabel('y label')
fig.savefig('image_output.png', dpi=300, format='png', bbox_extra_artists=(lgd,), bbox_inches='tight')
I wish that matplotlib would natively allow outside location for the legend box as Matlab does:
figure
x = 0:.2:12;
plot(x,besselj(1,x),x,besselj(2,x),x,besselj(3,x));
hleg = legend('First','Second','Third',...
'Location','NorthEastOutside')
% Make the text of the legend italic and color it brown
set(hleg,'FontAngle','italic','TextColor',[.3,.2,.1])

In addition to all the excellent answers here, newer versions of matplotlib and pylab can automatically determine where to put the legend without interfering with the plots, if possible.
pylab.legend(loc='best')
This will automatically place the legend away from the data if possible!
However, if there isn't any place to put the legend without overlapping the data, then you'll want to try one of the other answers; using loc="best" will never put the legend outside of the plot.

Short Answer: Invoke draggable on the legend and interactively move it wherever you want:
ax.legend().draggable()
Long Answer: If you rather prefer to place the legend interactively/manually rather than programmatically, you can toggle the draggable mode of the legend so that you can drag it to wherever you want. Check the example below:
import matplotlib.pylab as plt
import numpy as np
#define the figure and get an axes instance
fig = plt.figure()
ax = fig.add_subplot(111)
#plot the data
x = np.arange(-5, 6)
ax.plot(x, x*x, label='y = x^2')
ax.plot(x, x*x*x, label='y = x^3')
ax.legend().draggable()
plt.show()

Newer versions of Matplotlib have made it much easier to position the legend outside the plot. I produced this example with Matplotlib version 3.1.1.
Users can pass a 2-tuple of coordinates to the loc parameter to position the legend anywhere in the bounding box. The only gotcha is you need to run plt.tight_layout() to get matplotlib to recompute the plot dimensions so the legend is visible:
import matplotlib.pyplot as plt
plt.plot([0, 1], [0, 1], label="Label 1")
plt.plot([0, 1], [0, 2], label='Label 2')
plt.legend(loc=(1.05, 0.5))
plt.tight_layout()
This leads to the following plot:
References:
matplotlib.pyplot.legend

It is not exactly what you asked for, but I found it's an alternative for the same problem.
Make the legend semitransparent, like so:
Do this with:
fig = pylab.figure()
ax = fig.add_subplot(111)
ax.plot(x, y, label=label, color=color)
# Make the legend transparent:
ax.legend(loc=2, fontsize=10, fancybox=True).get_frame().set_alpha(0.5)
# Make a transparent text box
ax.text(0.02, 0.02, yourstring, verticalalignment='bottom',
horizontalalignment='left',
fontsize=10,
bbox={'facecolor':'white', 'alpha':0.6, 'pad':10},
transform=self.ax.transAxes)

As noted, you could also place the legend in the plot, or slightly off it to the edge as well. Here is an example using the Plotly Python API, made with an IPython Notebook. I'm on the team.
To begin, you'll want to install the necessary packages:
import plotly
import math
import random
import numpy as np
Then, install Plotly:
un='IPython.Demo'
k='1fw3zw2o13'
py = plotly.plotly(username=un, key=k)
def sin(x,n):
sine = 0
for i in range(n):
sign = (-1)**i
sine = sine + ((x**(2.0*i+1))/math.factorial(2*i+1))*sign
return sine
x = np.arange(-12,12,0.1)
anno = {
'text': '$\\sum_{k=0}^{\\infty} \\frac {(-1)^k x^{1+2k}}{(1 + 2k)!}$',
'x': 0.3, 'y': 0.6,'xref': "paper", 'yref': "paper",'showarrow': False,
'font':{'size':24}
}
l = {
'annotations': [anno],
'title': 'Taylor series of sine',
'xaxis':{'ticks':'','linecolor':'white','showgrid':False,'zeroline':False},
'yaxis':{'ticks':'','linecolor':'white','showgrid':False,'zeroline':False},
'legend':{'font':{'size':16},'bordercolor':'white','bgcolor':'#fcfcfc'}
}
py.iplot([{'x':x, 'y':sin(x,1), 'line':{'color':'#e377c2'}, 'name':'$x\\\\$'},\
{'x':x, 'y':sin(x,2), 'line':{'color':'#7f7f7f'},'name':'$ x-\\frac{x^3}{6}$'},\
{'x':x, 'y':sin(x,3), 'line':{'color':'#bcbd22'},'name':'$ x-\\frac{x^3}{6}+\\frac{x^5}{120}$'},\
{'x':x, 'y':sin(x,4), 'line':{'color':'#17becf'},'name':'$ x-\\frac{x^5}{120}$'}], layout=l)
This creates your graph, and allows you a chance to keep the legend within the plot itself. The default for the legend if it is not set is to place it in the plot, as shown here.
For an alternative placement, you can closely align the edge of the graph and border of the legend, and remove border lines for a closer fit.
You can move and re-style the legend and graph with code, or with the GUI. To shift the legend, you have the following options to position the legend inside the graph by assigning x and y values of <= 1. E.g :
{"x" : 0,"y" : 0} -- Bottom Left
{"x" : 1, "y" : 0} -- Bottom Right
{"x" : 1, "y" : 1} -- Top Right
{"x" : 0, "y" : 1} -- Top Left
{"x" :.5, "y" : 0} -- Bottom Center
{"x": .5, "y" : 1} -- Top Center
In this case, we choose the upper right, legendstyle = {"x" : 1, "y" : 1}, also described in the documentation:

I simply used the string 'center left' for the location, like in MATLAB.
I imported pylab from Matplotlib.
See the code as follows:
from matplotlib as plt
from matplotlib.font_manager import FontProperties
t = A[:, 0]
sensors = A[:, index_lst]
for i in range(sensors.shape[1]):
plt.plot(t, sensors[:, i])
plt.xlabel('s')
plt.ylabel('°C')
lgd = plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fancybox = True, shadow = True)

You can also try figlegend. It is possible to create a legend independent of any Axes object. However, you may need to create some "dummy" Paths to make sure the formatting for the objects gets passed on correctly.

Something along these lines worked for me. Starting with a bit of code taken from Joe, this method modifies the window width to automatically fit a legend to the right of the figure.
import matplotlib.pyplot as plt
import numpy as np
plt.ion()
x = np.arange(10)
fig = plt.figure()
ax = plt.subplot(111)
for i in xrange(5):
ax.plot(x, i * x, label='$y = %ix$'%i)
# Put a legend to the right of the current axis
leg = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.draw()
# Get the ax dimensions.
box = ax.get_position()
xlocs = (box.x0,box.x1)
ylocs = (box.y0,box.y1)
# Get the figure size in inches and the dpi.
w, h = fig.get_size_inches()
dpi = fig.get_dpi()
# Get the legend size, calculate new window width and change the figure size.
legWidth = leg.get_window_extent().width
winWidthNew = w*dpi+legWidth
fig.set_size_inches(winWidthNew/dpi,h)
# Adjust the window size to fit the figure.
mgr = plt.get_current_fig_manager()
mgr.window.wm_geometry("%ix%i"%(winWidthNew,mgr.window.winfo_height()))
# Rescale the ax to keep its original size.
factor = w*dpi/winWidthNew
x0 = xlocs[0]*factor
x1 = xlocs[1]*factor
width = box.width*factor
ax.set_position([x0,ylocs[0],x1-x0,ylocs[1]-ylocs[0]])
plt.draw()

New in matplotlib 3.7
Legends now accept "outside" locations directly, e.g., loc='outside right upper'.
Just make sure the layout is constrained and then prepend "outside" to the loc string:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(layout='constrained')
# --------------------
x = np.linspace(-np.pi, np.pi)
ax.plot(x, x, label='$f(x) = x$')
ax.plot(x, np.sin(x), label='$f(x) = sin(x)$')
ax.plot(x, np.cos(x), label='$f(x) = cos(x)$')
fig.legend(loc='outside right upper')
# -------
plt.show()
Multiple subplots also work fine with the new "outside" locations:
fig, (ax1, ax2) = plt.subplots(1, 2, layout='constrained')
# --------------------
x = np.linspace(-np.pi, np.pi)
ax1.plot(x, x, '-', label='$f(x) = x$')
ax1.plot(x, np.sin(x), '--', label='$f(x) = sin(x)$')
ax2.plot(x, np.cos(x), ':', label='$f(x) = cos(x)$')
fig.legend(loc='outside right center')
# -------
Of course the available "outside" locations are preset, so use the older answers if you need finer positioning. However the standard locations should fit most use cases:
locs = [
'outside upper left', 'outside upper center', 'outside upper right',
'outside center right', 'upper center left',
'outside lower right', 'outside lower center', 'outside lower left',
]
for loc in locs:
fig.legend(loc=loc, title=loc)
locs = [
'outside right upper', 'outside right lower',
'outside left lower', 'outside left upper',
]
for loc in locs:
fig.legend(loc=loc, title=loc)

The solution that worked for me when I had a huge legend was to use an extra empty image layout.
In the following example, I made four rows and at the bottom I plotted the image with an offset for the legend (bbox_to_anchor). At the top it does not get cut.
f = plt.figure()
ax = f.add_subplot(414)
lgd = ax.legend(loc='upper left', bbox_to_anchor=(0, 4), mode="expand", borderaxespad=0.3)
ax.autoscale_view()
plt.savefig(fig_name, format='svg', dpi=1200, bbox_extra_artists=(lgd,), bbox_inches='tight')

Here's another solution, similar to adding bbox_extra_artists and bbox_inches, where you don't have to have your extra artists in the scope of your savefig call. I came up with this since I generate most of my plot inside functions.
Instead of adding all your additions to the bounding box when you want to write it out, you can add them ahead of time to the Figure's artists. Using something similar to Franck Dernoncourt's answer:
import matplotlib.pyplot as plt
# Data
all_x = [10, 20, 30]
all_y = [[1, 3], [1.5, 2.9], [3, 2]]
# Plotting function
def gen_plot(x, y):
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(all_x, all_y)
lgd = ax.legend(["Lag " + str(lag) for lag in all_x], loc="center right", bbox_to_anchor=(1.3, 0.5))
fig.artists.append(lgd) # Here's the change
ax.set_title("Title")
ax.set_xlabel("x label")
ax.set_ylabel("y label")
return fig
# Plotting
fig = gen_plot(all_x, all_y)
# No need for `bbox_extra_artists`
fig.savefig("image_output.png", dpi=300, format="png", bbox_inches="tight")
.

Here is an example from the matplotlib tutorial found here. This is one of the more simpler examples but I added transparency to the legend and added plt.show() so you can paste this into the interactive shell and get a result:
import matplotlib.pyplot as plt
p1, = plt.plot([1, 2, 3])
p2, = plt.plot([3, 2, 1])
p3, = plt.plot([2, 3, 1])
plt.legend([p2, p1, p3], ["line 1", "line 2", "line 3"]).get_frame().set_alpha(0.5)
plt.show()

Read height of legend in Python

I have some plots with a lot of information and lines, so sometimes I tend to put the legend outside the plot itself using bbox_to_anchor. I also prefer to have a title of the plot, but this will positionally coincide with the legend in that case. The following example below is just an illustration of the problem.
import numpy as np
import matplotlib.pyplot as plt
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)
r = 1 + np.sin(4 * np.pi * t)
q = 1 + np.sin(6 * np.pi * t)
fig, ax = plt.subplots()
ax.plot(t, s, label='S')
ax.plot(t, r, label='R')
ax.plot(t, q, label='Q')
leg = ax.legend(loc=3, ncol=3, bbox_to_anchor=(.0, 1.02, 1., .102), borderaxespad=0., mode='expand')
ax.set_title('SIMPLE PLOT', y=1.1)
plt.show()
To avoid this, I set some kind of y-value (e.g. y=1.1). I would like to automate this process because I keep updating the same plot with new data, so the legend grows in size, and I need to adjust the position of the title accordingly.
Is there a way to automate this process?
Is there a function in Python that is able to read the height of the legend so that this can be used to adjust the title position?

The height of the legend is determined at draw time. You can get it after having drawn the figure via legend.get_window_extent(). The resulting bounding box is in units of pixels. In order to find the offset of the title, you will need to subtract the upper limit of the legend from the upper limit of the axes. So you need to get the axes position in pixels as well.
The title can be offset either in figure coordinates (y=1.1) or points (pad=20). I would suggest to use points here, to make it independent of the size of the axes. So you can calculate the difference in upper positions, convert from pixels to points (i.e. distance [pixels] * ppi / dpi) and add some constant offset in points (because usually you would not want the title to sit exactly on the border of the legend). Then use that number as pad.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(constrained_layout=True)
ax.plot([1,2,3], np.random.rand(3,5), label='Label')
leg = ax.legend(loc="lower center", ncol=3, bbox_to_anchor=(.0, 1.02, 1., 1.02),
borderaxespad=0, mode='expand')
fig.canvas.draw()
leg_box = leg.get_window_extent()
ax_box = ax.get_position().transformed(fig.transFigure)
pad = (leg_box.y1 - ax_box.y1)*72./fig.dpi + 6
ax.set_title('SIMPLE PLOT', pad=pad)
plt.show()
Note that here I also used constrained_layout to have the title not cropped by the figure boundaries.

Tick properties for scatterplot matrices with Matplotlib

I am trying to plot a scatterplot matrix based on the code written by Joe Kington: Is there a function to make scatterplot matrices in matplotlib?
Some people already helped me: Thank you again (especially J.K.).
I am having a last problem: I cannot rotate the ticks of some axis for which numbers overlap (bottom left):
I would like to try to have them vertical but I cannot do it.... Here is my code:
import itertools
import numpy as np
import pylab as plot
import scipy
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import axis
import math
from matplotlib import rc
import os
import platform
def main():
FigSize=8.89
FontSize=8
np.random.seed(1977)
numvars, numdata = 4, 10
data = 10 * np.random.random((numvars, numdata))
fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'], FigSize, FontSize,
linestyle='none', marker='o', color='black', mfc='none', markersize=3,)
fig.suptitle('Simple Scatterplot Matrix')
plt.savefig('Plots/ScatterplotMatrix/ScatterplotMatrix2.pdf',format='pdf', dpi=1000, transparent=True, bbox_inches='tight')
plt.show()
def scatterplot_matrix(data, names, FigSize, FontSize, **kwargs):
"""Plots a scatterplot matrix of subplots. Each row of "data" is plotted
against other rows, resulting in a nrows by nrows grid of subplots with the
diagonal subplots labeled with "names". Additional keyword arguments are
passed on to matplotlib's "plot" command. Returns the matplotlib figure
object containg the subplot grid."""
legend=['(kPa)','\%','\%','\%']
numvars, numdata = data.shape
fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(FigSize/2.54,FigSize/2.54))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
sub_labelx_top=[2,4]
sub_labelx_bottom=[13,15]
sub_labely_left=[5,13]
sub_labely_right=[4,12]
for i, ax in enumerate(axes.flat, start=1):
# Hide all ticks and labels
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.xaxis.set_major_locator(MaxNLocator(prune='both',nbins=4))
ax.yaxis.set_major_locator(MaxNLocator(prune='both',nbins=4)) #http://matplotlib.org/api/ticker_api.html#matplotlib.ticker.MaxNLocator
# Set up ticks only on one side for the "edge" subplots...
if ax.is_first_col():
ax.yaxis.set_ticks_position('left')
ax.tick_params(direction='out')
ax.yaxis.set_tick_params(labelsize=0.75*FontSize)
if i in sub_labely_left:
ax.yaxis.set_label_position('left')
ax.set_ylabel('(\%)',fontsize=0.75*FontSize)
if ax.is_last_col():
ax.yaxis.set_ticks_position('right')
ax.tick_params(direction='out')
ax.yaxis.set_tick_params(labelsize=0.75*FontSize)
if i in sub_labely_right:
ax.yaxis.set_label_position('right')
if i==4:
ax.set_ylabel('(kPa)',fontsize=0.75*FontSize)
else:
ax.set_ylabel('(\%)',fontsize=0.75*FontSize)
if ax.is_first_row():
ax.xaxis.set_ticks_position('top')
ax.tick_params(direction='out')
ax.xaxis.set_tick_params(labelsize=0.75*FontSize)
if i in sub_labelx_top:
ax.xaxis.set_label_position('top')
ax.set_xlabel('(\%)',fontsize=0.75*FontSize)
if ax.is_last_row():
ax.xaxis.set_ticks_position('bottom')
ax.tick_params(direction='out')
ax.xaxis.set_tick_params(labelsize=0.75*FontSize)
if i in sub_labelx_bottom:
ax.xaxis.set_label_position('bottom')
if i==13:
ax.set_xlabel('(kPa)',fontsize=0.75*FontSize)
else:
ax.set_xlabel('(\%)',fontsize=0.75*FontSize)
# Plot the data.
for i, j in zip(*np.triu_indices_from(axes, k=1)):
for x, y in [(i,j), (j,i)]:
axes[x,y].plot(data[y], data[x], **kwargs)
# Label the diagonal subplots...
for i, label in enumerate(names):
axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
ha='center', va='center',fontsize=FontSize)
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
return fig
main()
My second question is more for the 'fun': how can I make the subplots perfectly squares?
I apologize to Joe Kington; I know my code is way less elegant than his... I just started few weeks ago. If you have any suggestions to improve mine, for example to make it more dynamic, I am very interesting.

You can rotate the xtick labels using setp.
from matplotlib.artist import setp
Then after you set the x tick positions for the top row and left column of subplot call:
setp(ax.get_xticklabels(), rotation=90)
To make the size of the subplots equal, you can fig.subplots_adjust to set the area of all the subplots to a square. Something like this:
gridSize = 0.6
leftBound = 0.5 - gridSize/2
bottomBound = 0.1
rightBound = leftBound + gridSize
topBound = bottomBound + gridSize
fig.subplots_adjust(hspace=0.05, wspace=0.05, left=leftBound,
bottom=bottomBound, right=rightBound, top=topBound)
If the figure size isn't square, you'll need to change the shape of the grid accordingly. Alternately, you could add each subplot axes individually with fig.add_axes. That will allow you to set the size directly but you'll also have to set the location.
Don't use bbox_inches='tight' to save the figure or you'll lose the title with these setting. You can save like this:
plt.savefig('ScatterplotMatrix.pdf',format='pdf', dpi=1000, transparent=True)
The resulting graph looks like this:

tick label positions for matplotlib 3D plot

I am trying to work out how to set/correct the position of tick labels for a 3D matplotlib plot. Tick labels do not align with the ticks. The issue seems to be especially prominent when many tick labels are required.
I have modified an example (http://matplotlib.org/examples/mplot3d/polys3d_demo.html) from the matplotlib documentation to illustrate my question.
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PolyCollection
from matplotlib.colors import colorConverter
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(10,10))
ax = fig.gca(projection='3d')
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
xs = np.arange(0, 10, 0.4)
verts = []
zs = np.arange(50)
for z in zs:
ys = np.ones(len(xs))*z
ys[0], ys[-1] = 0, 0
verts.append(list(zip(xs, ys)))
poly = PolyCollection(verts,facecolor='c')
poly.set_alpha(0.7)
ax.add_collection3d(poly, zs=zs, zdir='y')
ax.set_xlabel('X')
ax.set_xlim3d(0, 10)
ax.set_ylabel('Y')
ax.set_ylim3d(-1, len(zs))
ax.set_yticks(np.arange(len(zs)))
labels = {}
for l_c in zs:
labels[l_c] = 'This Looks Bad'
ax.set_yticklabels(labels,rotation=-15)
ax.set_zlabel('Z')
ax.set_zlim3d(0, ys.max())
plt.show()
So the question is: how can I get the tick labels to align with the tick positions?

By using these alignments, I get much better placements:
ax.set_yticklabels(labels,rotation=-15,
verticalalignment='baseline',
horizontalalignment='left')
I've modified the example with less tick markers so you can see the placement:

They do align, but with the horizontal position centered at the tick. Because of the 3D view this makes them appear a bit below where you would expect them to be. The effect is not related to the amount of ticks but to the width.
Specifically setting the alignment will help. Try adding:
ax.set_yticklabels(labels,rotation=-15, va='center', ha='left')
Play around a bit with the different alignments to see which you prefer, i think you're after ha='left'.
Reducing the padding, distance from the tick, might also help.

You can also set the pad argument as negative in the tick_params options for each axis. Like this:
ax.tick_params(axis='x', which='major', pad=-3)
This might help to adjust the distance between tick labels and axis.