The following code will generate a legend with duplicated labels. How to remove the duplicated one, so that there is only 1 label1 and 1 label2? One possible approach is to remove the duplicated item in the lines_labels list, but I couldn't figure out the code. Can someone please help? Thanks a lot!
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
ax1.scatter(1,2,label='a',color='black')
ax1.plot(np.array([1, 2]), np.array([1, 2]),color='b',label='xvalues')
ax2.scatter(3,4,label='a',color='black')
ax3.scatter(5,6,label='b',color='red')
ax4.scatter(7,8,label='b',color='red')
lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
fig.legend(lines, labels, scatterpoints = 1)
plt.show()
you could put all labels and lines in a dict and filter only for unique labels like this
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
ax1.scatter(1, 2, label='a', color='black')
ax1.plot(np.array([1, 2]), np.array([1, 2]), color='b', label='xvalues')
ax2.scatter(3, 4, label='a', color='black')
ax3.scatter(5, 6, label='b', color='red')
ax4.scatter(7, 8, label='b', color='red')
lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]
lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
# grab unique labels
unique_labels = set(labels)
# assign labels and legends in dict
legend_dict = dict(zip(labels, lines))
# query dict based on unique labels
unique_lines = [legend_dict[x] for x in unique_labels]
fig.legend(unique_lines, unique_labels, scatterpoints=1)
plt.show()
I would like to create a plot that consists of three subplots, where the upper left plot has the same width as the lower left plot but 1/3 of the height. Besides, I'd also like to plot the legend in the upper right area from the lower left plot. Is this even possible?
fig, ax = plt.subplots(2, figsize = (16,9))
ax1 = plt.subplot2grid((2,3), (1,0), colspan=2)
ax2 = plt.subplot2grid((2,3), (1,2), colspan=1)
ax3 = plt.subplot2grid((2,3), (0,0), colspan=2)
fig.suptitle('Title')
fig.tight_layout()
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
x = np.linspace(0, 2*np.pi)
y1 = np.cos(x)
y2 = np.sin(x)
fig = plt.figure()
gs = GridSpec(2, 2, width_ratios=[2, 1], height_ratios=[1, 3])
ax1 = fig.add_subplot(gs[0])
ax2 = fig.add_subplot(gs[1])
ax3 = fig.add_subplot(gs[2])
ax4 = fig.add_subplot(gs[3])
ax3.plot(x, y1, label="cos")
ax3.plot(x, y2, label="sin")
handles, labels = ax3.get_legend_handles_labels()
# hide axis on the top left subplot
ax2.axis("off")
# adding two legends
legend1 = ax2.legend([handles[0]], [labels[0]], loc="upper left")
legend2 = ax2.legend([handles[1]], [labels[1]], loc="lower right")
ax2.add_artist(legend1)
plt.tight_layout()
I have a DataFrame with three numerical variables Porosity, Perm and AI. I would like to make a subplot and in each plot, I would like the histogram of the three variables, by a categorical variable 'Facies'. Facies can take only two values: Sand and Shale.
In summary, each subplot needs a histogram and each histogram must be drawn based in the categorical variable Facies, to make a comparison between facies.
So far, I can make it work, but I cannot add the axis title to each subplot.
plt.subplot(311)
plt.hist(df_sd['Porosity'].values, label='Sand', bins=30, alpha=0.6)
plt.hist(df_sh['Porosity'].values, label='Shale', bins=30, alpha=0.6)
ax.set(xlabel='Porosity (fraction)', ylabel='Density', title='Porosity
Histogram')
plt.legend()
plt.subplot(312)
plt.hist(df_sd['log10Perm'].values, label='Sand', bins=30, alpha=0.6,)
plt.hist(df_sh['log10Perm'].values, label='Shale', bins=30, alpha=0.6)
ax.set(xlabel='Permeability (mD)', ylabel='Density', title='Permeability
Histogram')
plt.legend()
plt.subplot(313)
plt.hist(df_sd['AI'].values, label='Sand', bins=30, alpha=0.6)
plt.hist(df_sh['AI'].values, label='Shale', bins=30, alpha=0.6)
ax.set(xlabel='AI (units)', ylabel='Density', title='Acoustic Impedance
Histogram')
plt.legend()
plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=3.5, wspace=0.1,
hspace=0.2);
#I have tried with:
fig, axs = plt.subplots(2, 1)
but when I code
axs[0].hist(df_sd['Porosity'].values, label='Sand', bins=30, alpha=0.6)
axs[0].hist(df_sd['Porosity'].values, label='Shale', bins=30, alpha=0.6)
#But the histogram for shale overrides the histogram for Sand.
I would like to have this result but with both x and y axis with label names. Furthermore, it would be helpful to have a title for each subplot.
I just did a subplot with contours, but I think the framework will be very similar:
fig, axs = plt.subplots(2, 2, constrained_layout=True)
for ax, extend in zip(axs.ravel(), extends):
cs = ax.contourf(X, Y, Z, levels, cmap=cmap, extend=extend, origin=origin)
fig.colorbar(cs, ax=ax, shrink=0.9)
ax.set_title("extend = %s" % extend)
ax.locator_params(nbins=4)
plt.show()
I think the main point to note (and this I learned from the link below) is their use of zip(axs.ravel()) in the for loop to establish each ax and then plot what you wish on that ax. I'm fairly certain you can adapt this for your uses.
The full example is available at: https://matplotlib.org/gallery/images_contours_and_fields/contourf_demo.html#sphx-glr-gallery-images-contours-and-fields-contourf-demo-py
I have found an answer:
fig = plt.figure()
ax = fig.add_subplot(111)
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312)
ax2 = fig.add_subplot(313)
plt.subplot(311)
ax1.hist(df_sd['Porosity'].values, label='Sand', bins=30, alpha=0.6)
ax1.hist(df_sh['Porosity'].values, label='Shale', bins=30, alpha=0.6)
ax1.set(xlabel='Porosity (fraction)', ylabel='Density', title='Porosity Histogram')
ax1.legend()
I have a set of subplot's that display different information. For the example below, I can assign the scatter plot to the designated subplot but the two distplot occupy the last subplot created.
import matplotlib.pyplot as plt
import seaborn as sns
x = [1,4,5,6,7,8]
x2 = [3,4,8,2,8,8]
y = [1,2,4,8,1,9]
def L_plot(ax, fontsize=12):
ax.set_xlabel('x-label', fontsize=8)
ax.set_ylabel('y-label', fontsize=8)
ax.set_title('L', fontsize=10)
ax.grid(False)
ax.scatter(x, y)
def E_plot(ax2,pid, fontsize=12):
ax2.set_xlabel('x-label', fontsize=8)
ax2.set_ylabel('y-label', fontsize=8)
ax2.set_title('E', fontsize=10)
ax2.grid(False)
ax2 = sns.distplot(x, kde=False, norm_hist=True, color='b', bins = 10)
def D_plot(ax,pid, fontsize=12):
ax.set_xlabel('x-label', fontsize=8)
ax.set_ylabel('y-label', fontsize=8)
ax.set_title('D', fontsize=10)
ax.grid(False)
ax = sns.distplot(x2, kde=False, norm_hist=True, color='b', bins = 10)
ax1 = plt.subplot2grid((3,1), (0, 0))
ax2 = plt.subplot2grid((3,1), (1, 0))
ax3 = plt.subplot2grid((3,1), (2, 0))
L_plot(ax1,1)
E_plot(ax2,1)
D_plot(ax3,1)
plt.tight_layout()
plt.show()
I'm trying to assign E_plot to the subplot in the second row but both distplot's are located in the last subplot created.
I'm not sure if the seaboard packages can't be assigned or I'm not correctly assigning it?
The call signature for distplot is:
seaborn.distplot(a, bins=None, hist=True, kde=True, rug=False,
fit=None, hist_kws=None, kde_kws=None, rug_kws=None,
fit_kws=None, color=None, vertical=False,
norm_hist=False, axlabel=None, label=None,
ax=None)
Notice the last option. If you don't tell it which Axes object to use, it'll use the one returned by pyplot.gca() (gca = "get current Axes").
So you need to do, e.g.,
sns.distplot(x2, kde=False, norm_hist=True, color='b', bins=10, ax=ax2)
I have a plot with two y-axes, using twinx(). I also give labels to the lines, and want to show them with legend(), but I only succeed to get the labels of one axis in the legend:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(time, Swdown, '-', label = 'Swdown')
ax.plot(time, Rn, '-', label = 'Rn')
ax2 = ax.twinx()
ax2.plot(time, temp, '-r', label = 'temp')
ax.legend(loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
So I only get the labels of the first axis in the legend, and not the label 'temp' of the second axis. How could I add this third label to the legend?
You can easily add a second legend by adding the line:
ax2.legend(loc=0)
You'll get this:
But if you want all labels on one legend then you should do something like this:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
time = np.arange(10)
temp = np.random.random(10)*30
Swdown = np.random.random(10)*100-10
Rn = np.random.random(10)*100-10
fig = plt.figure()
ax = fig.add_subplot(111)
lns1 = ax.plot(time, Swdown, '-', label = 'Swdown')
lns2 = ax.plot(time, Rn, '-', label = 'Rn')
ax2 = ax.twinx()
lns3 = ax2.plot(time, temp, '-r', label = 'temp')
# added these three lines
lns = lns1+lns2+lns3
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
Which will give you this:
I'm not sure if this functionality is new, but you can also use the get_legend_handles_labels() method rather than keeping track of lines and labels yourself:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
pi = np.pi
# fake data
time = np.linspace (0, 25, 50)
temp = 50 / np.sqrt (2 * pi * 3**2) \
* np.exp (-((time - 13)**2 / (3**2))**2) + 15
Swdown = 400 / np.sqrt (2 * pi * 3**2) * np.exp (-((time - 13)**2 / (3**2))**2)
Rn = Swdown - 10
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(time, Swdown, '-', label = 'Swdown')
ax.plot(time, Rn, '-', label = 'Rn')
ax2 = ax.twinx()
ax2.plot(time, temp, '-r', label = 'temp')
# ask matplotlib for the plotted objects and their labels
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
From matplotlib version 2.1 onwards, you may use a figure legend. Instead of ax.legend(), which produces a legend with the handles from the axes ax, one can create a figure legend
fig.legend(loc="upper right")
which will gather all handles from all subplots in the figure. Since it is a figure legend, it will be placed at the corner of the figure, and the loc argument is relative to the figure.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,10)
y = np.linspace(0,10)
z = np.sin(x/3)**2*98
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y, '-', label = 'Quantity 1')
ax2 = ax.twinx()
ax2.plot(x,z, '-r', label = 'Quantity 2')
fig.legend(loc="upper right")
ax.set_xlabel("x [units]")
ax.set_ylabel(r"Quantity 1")
ax2.set_ylabel(r"Quantity 2")
plt.show()
In order to place the legend back into the axes, one would supply a bbox_to_anchor and a bbox_transform. The latter would be the axes transform of the axes the legend should reside in. The former may be the coordinates of the edge defined by loc given in axes coordinates.
fig.legend(loc="upper right", bbox_to_anchor=(1,1), bbox_transform=ax.transAxes)
You can easily get what you want by adding the line in ax:
ax.plot([], [], '-r', label = 'temp')
or
ax.plot(np.nan, '-r', label = 'temp')
This would plot nothing but add a label to legend of ax.
I think this is a much easier way.
It's not necessary to track lines automatically when you have only a few lines in the second axes, as fixing by hand like above would be quite easy. Anyway, it depends on what you need.
The whole code is as below:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
time = np.arange(22.)
temp = 20*np.random.rand(22)
Swdown = 10*np.random.randn(22)+40
Rn = 40*np.random.rand(22)
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twinx()
#---------- look at below -----------
ax.plot(time, Swdown, '-', label = 'Swdown')
ax.plot(time, Rn, '-', label = 'Rn')
ax2.plot(time, temp, '-r') # The true line in ax2
ax.plot(np.nan, '-r', label = 'temp') # Make an agent in ax
ax.legend(loc=0)
#---------------done-----------------
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
The plot is as below:
Update: add a better version:
ax.plot(np.nan, '-r', label = 'temp')
This will do nothing while plot(0, 0) may change the axis range.
One extra example for scatter
ax.scatter([], [], s=100, label = 'temp') # Make an agent in ax
ax2.scatter(time, temp, s=10) # The true scatter in ax2
ax.legend(loc=1, framealpha=1)
Preparation
import numpy as np
from matplotlib import pyplot as plt
fig, ax1 = plt.subplots( figsize=(15,6) )
Y1, Y2 = np.random.random((2,100))
ax2 = ax1.twinx()
Content
I'm surprised it did not show up so far but the simplest way is to either collect them manually into one of the axes objs (that lie on top of each other)
l1 = ax1.plot( range(len(Y1)), Y1, label='Label 1' )
l2 = ax2.plot( range(len(Y2)), Y2, label='Label 2', color='orange' )
ax1.legend( handles=l1+l2 )
or have them collected automatically into the surrounding figure by fig.legend() and fiddle around with the the bbox_to_anchor parameter:
ax1.plot( range(len(Y1)), Y1, label='Label 1' )
ax2.plot( range(len(Y2)), Y2, label='Label 2', color='orange' )
fig.legend( bbox_to_anchor=(.97, .97) )
Finalization
fig.tight_layout()
fig.savefig('stackoverflow.png', bbox_inches='tight')
A quick hack that may suit your needs..
Take off the frame of the box and manually position the two legends next to each other. Something like this..
ax1.legend(loc = (.75,.1), frameon = False)
ax2.legend( loc = (.75, .05), frameon = False)
Where the loc tuple is left-to-right and bottom-to-top percentages that represent the location in the chart.
I found an following official matplotlib example that uses host_subplot to display multiple y-axes and all the different labels in one legend. No workaround necessary. Best solution I found so far.
http://matplotlib.org/examples/axes_grid/demo_parasite_axes2.html
from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
import matplotlib.pyplot as plt
host = host_subplot(111, axes_class=AA.Axes)
plt.subplots_adjust(right=0.75)
par1 = host.twinx()
par2 = host.twinx()
offset = 60
new_fixed_axis = par2.get_grid_helper().new_fixed_axis
par2.axis["right"] = new_fixed_axis(loc="right",
axes=par2,
offset=(offset, 0))
par2.axis["right"].toggle(all=True)
host.set_xlim(0, 2)
host.set_ylim(0, 2)
host.set_xlabel("Distance")
host.set_ylabel("Density")
par1.set_ylabel("Temperature")
par2.set_ylabel("Velocity")
p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature")
p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")
par1.set_ylim(0, 4)
par2.set_ylim(1, 65)
host.legend()
plt.draw()
plt.show()
If you are using Seaborn you can do this:
g = sns.barplot('arguments blah blah')
g2 = sns.lineplot('arguments blah blah')
h1,l1 = g.get_legend_handles_labels()
h2,l2 = g2.get_legend_handles_labels()
#Merging two legends
g.legend(h1+h2, l1+l2, title_fontsize='10')
#removes the second legend
g2.get_legend().remove()
As provided in the example from matplotlib.org, a clean way to implement a single legend from multiple axes is with plot handles:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
fig.subplots_adjust(right=0.75)
twin1 = ax.twinx()
twin2 = ax.twinx()
# Offset the right spine of twin2. The ticks and label have already been
# placed on the right by twinx above.
twin2.spines.right.set_position(("axes", 1.2))
p1, = ax.plot([0, 1, 2], [0, 1, 2], "b-", label="Density")
p2, = twin1.plot([0, 1, 2], [0, 3, 2], "r-", label="Temperature")
p3, = twin2.plot([0, 1, 2], [50, 30, 15], "g-", label="Velocity")
ax.set_xlim(0, 2)
ax.set_ylim(0, 2)
twin1.set_ylim(0, 4)
twin2.set_ylim(1, 65)
ax.set_xlabel("Distance")
ax.set_ylabel("Density")
twin1.set_ylabel("Temperature")
twin2.set_ylabel("Velocity")
ax.yaxis.label.set_color(p1.get_color())
twin1.yaxis.label.set_color(p2.get_color())
twin2.yaxis.label.set_color(p3.get_color())
tkw = dict(size=4, width=1.5)
ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
ax.tick_params(axis='x', **tkw)
ax.legend(handles=[p1, p2, p3])
plt.show()
Here is another way to do this:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
fig = plt.figure()
ax = fig.add_subplot(111)
pl_1, = ax.plot(time, Swdown, '-')
label_1 = 'Swdown'
pl_2, = ax.plot(time, Rn, '-')
label_2 = 'Rn'
ax2 = ax.twinx()
pl_3, = ax2.plot(time, temp, '-r')
label_3 = 'temp'
ax.legend([pl[enter image description here][1]_1, pl_2, pl_3], [label_1, label_2, label_3], loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
enter image description here
The solutions proposed so far have one or two inconvenients:
Handles needs to be collected individually when plotting, e.g. lns1 = ax.plot(time, Swdown, '-', label = 'Swdown'). There is a risk of forgetting handles when updating the code.
Legend is drawn for the whole figure, not by subplot, which is likely a no-go if you have multiple subplots.
This new solution takes advantage of Axes.get_legend_handles_labels() to collect existing handles and labels for the main axis and for the twin axis.
Collecting handles and labels automatically
This numpy operation will scan all axes which share the same subplot area than ax, including ax and return merged handles and labels:
hl = np.hstack([axis.get_legend_handles_labels()
for axis in ax.figure.axes
if axis.bbox.bounds == ax.bbox.bounds])
It can be used to feed legend() arguments this way:
import numpy as np
import matplotlib.pyplot as plt
t = np.arange(1, 200)
signals = [np.exp(-t/20) * np.cos(t*k) for k in (1, 2)]
fig, axes = plt.subplots(nrows=2, figsize=(10, 3), layout='constrained')
axes = axes.flatten()
for i, (ax, signal) in enumerate(zip(axes, signals)):
# Plot as usual, no change to the code
ax.plot(t, signal, label=f'plotted on axes[{i}]', c='C0', lw=9, alpha=0.3)
ax2 = ax.twinx()
ax2.plot(t, signal, label=f'plotted on axes[{i}].twinx()', c='C1')
# The only specificity of the code is when plotting the legend
h, l = np.hstack([axis.get_legend_handles_labels()
for axis in ax.figure.axes
if axis.bbox.bounds == ax.bbox.bounds]).tolist()
ax2.legend(handles=h, labels=l, loc='upper right')