I am having diffculties to move the text "Rank" exactly one line above the first label and by not using guesswork as I have different chart types with variable sizes, widths and also paddings between the labels and bars.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from pylab import rcParams
rcParams['figure.figsize'] = 8, 6
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df = pd.DataFrame.from_records(zip(np.arange(1,30)))
df.plot.barh(width=0.8,ax=ax,legend=False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.tick_params(left=False, bottom=False)
ax.tick_params(axis='y', which='major', pad=36)
ax.set_title("Rankings")
ax.text(-5,30,"Rank")
plt.show()
Using transData.transform didn't get me any further. The problem seems to be that ax.text() with the position params of (0,0) aligns with the start of the bars and not the yticklabels which I need, so getting the exact position of yticklabels relative to the axis would be helpful.
The following approach creates an offset_copy transform, using "axes coordinates". The top left corner of the main plot is at position 0, 1 in axes coordinates. The ticks have a "pad" (between label and tick mark) and a "padding" (length of the tick mark), both measured in "points".
The text can be right aligned, just as the ticks. With "bottom" as vertical alignment, it will be just above the main plot. If that distance is too low, you could try ax.text(0, 1.01, ...) to have it a bit higher.
import matplotlib.pyplot as plt
from matplotlib.transforms import offset_copy
import pandas as pd
import numpy as np
from matplotlib import rcParams
rcParams['figure.figsize'] = 8, 6
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df = pd.DataFrame.from_records(zip(np.arange(1, 30)))
df.plot.barh(width=0.8, ax=ax, legend=False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.tick_params(left=False, bottom=False)
ax.tick_params(axis='y', which='major', pad=36)
ax.set_title("Rankings")
tick = ax.yaxis.get_major_ticks()[-1] # get information of one of the ticks
padding = tick.get_pad() + tick.get_tick_padding()
trans_offset = offset_copy(ax.transAxes, fig=fig, x=-padding, y=0, units='points')
ax.text(0, 1, "Rank", ha='right', va='bottom', transform=trans_offset)
# optionally also use tick.label.get_fontproperties()
plt.tight_layout()
plt.show()
I've answered my own question while Johan was had posted his one - which is pretty good and what I wanted. However, I post mine anyways as it uses an entirely different approach. Here I add a "ghost" row into the dataframe and label it appropriately which solves the problem:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from pylab import rcParams
rcParams['figure.figsize'] = 8, 6
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
df = pd.DataFrame.from_records(zip(np.arange(1,30)),columns=["val"])
#add a temporary header
new_row = pd.DataFrame({"val":0}, index=[0])
df = pd.concat([df[:],new_row]).reset_index(drop = True)
df.plot.barh(width=0.8,ax=ax,legend=False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.tick_params(left=False, bottom=False)
ax.tick_params(axis='y', which='major', pad=36)
ax.set_title("Rankings")
# Set the top label to "Rank"
yticklabels = [t for t in ax.get_yticklabels()]
yticklabels[-1]="Rank"
# Left align all labels
[t.set_ha("left") for t in ax.get_yticklabels()]
ax.set_yticklabels(yticklabels)
# delete the top bar effectively by setting it's height to 0
ax.patches[-1].set_height(0)
plt.show()
Perhaps the advantage is that it is always a constant distance above the top label, but with the disadvantage that this is a bit "patchy" in the most literal sense to transform your dataframe for this task.
I want to make 4 imshow subplots but all of them share the same colormap. Matplotlib automatically adjusts the scale on the colormap depending on the entries of the matrices. For example, if one of my matrices has all entires as 10 and the other one has all entries equal to 5 and I use the Greys colormap then one of my subplots should be completely black and the other one should be completely grey. But both of them end up becoming completely black. How to make all the subplots share the same scale on the colormap?
To get this right you need to have all the images with the same intensity scale, otherwise the colorbar() colours are meaningless. To do that, use the vmin and vmax arguments of imshow(), and make sure they are the same for all your images.
E.g., if the range of values you want to show goes from 0 to 10, you can use the following:
import pylab as plt
import numpy as np
my_image1 = np.linspace(0, 10, 10000).reshape(100,100)
my_image2 = np.sqrt(my_image1.T) + 3
plt.subplot(1, 2, 1)
plt.imshow(my_image1, vmin=0, vmax=10, cmap='jet', aspect='auto')
plt.subplot(1, 2, 2)
plt.imshow(my_image2, vmin=0, vmax=10, cmap='jet', aspect='auto')
plt.colorbar()
When the ranges of data (data1 and data2) sets are unknown and you want to use the same colour bar for both/all plots, find the overall minimum and maximum to use as vmin and vmax in the call to imshow:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=2)
# generate randomly populated arrays
data1 = np.random.rand(10,10)*10
data2 = np.random.rand(10,10)*10 -7.5
# find minimum of minima & maximum of maxima
minmin = np.min([np.min(data1), np.min(data2)])
maxmax = np.max([np.max(data1), np.max(data2)])
im1 = axes[0].imshow(data1, vmin=minmin, vmax=maxmax,
extent=(-5,5,-5,5), aspect='auto', cmap='viridis')
im2 = axes[1].imshow(data2, vmin=minmin, vmax=maxmax,
extent=(-5,5,-5,5), aspect='auto', cmap='viridis')
# add space for colour bar
fig.subplots_adjust(right=0.85)
cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7])
fig.colorbar(im2, cax=cbar_ax)
It may be that you don't know beforehand the ranges of your data, but you may know that somehow they are compatible. In that case, you may prefer to let matplotlib choose those ranges for the first plot and use the same range for the remaining plots. Here is how you can do it. The key is to get the limits with properties()['clim']
import numpy as np
import matplotlib.pyplot as plt
my_image1 = np.linspace(0, 10, 10000).reshape(100,100)
my_image2 = np.sqrt(my_image1.T) + 3
fig, axes = plt.subplots(nrows=1, ncols=2)
im = axes[0].imshow(my_image1)
clim=im.properties()['clim']
axes[1].imshow(my_image2, clim=clim)
fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.5)
plt.show()
I am trying to plot 5 charts one under the other with mplfinance.
This works:
for coin in coins:
mpf.plot(df_coins[coin], title=coin, type='line', volume=True, show_nontrading=True)
However each plot is a separate image in my Python Notebook cell output. And the x-axis labelling is repeated for each image.
I try to make a single figure containing multiple subplot/axis, and plot one chart into each axis:
from matplotlib import pyplot as plt
N = len(df_coins)
fig, axes = plt.subplots(N, figsize=(20, 5*N), sharex=True)
for i, ((coin, df), ax) in zip(enumerate(df_coins.items()), axes):
mpf.plot(df, ax=ax, title=coin, type='line', volume=True, show_nontrading=True)
This displays subfigures of the correct dimensions, however they are not getting populated with data. Axes are labelled from 0.0 to 1.0 and the title is not appearing.
What am I missing?
There are two ways to subplot. One is to set up a figure with mplfinance objects. The other way is to use your adopted matplotlib subplot to place it.
yfinace data
import matplotlib.pyplot as plt
import mplfinance as mpf
import yfinance as yf
tickers = ['AAPL','GOOG','TSLA']
data = yf.download(tickers, start="2021-01-01", end="2021-03-01", group_by='ticker')
aapl = data[('AAPL',)]
goog = data[('GOOG',)]
tsla = data[('TSLA',)]
mplfinance
fig = mpf.figure(style='yahoo', figsize=(12,9))
#fig.subplots_adjust(hspace=0.3)
ax1 = fig.add_subplot(3,1,1, sharex=ax3)
ax2 = fig.add_subplot(3,1,2, sharex=ax3)
ax3 = fig.add_subplot(3,1,3)
mpf.plot(aapl, type='line', ax=ax1, axtitle='AAPL', xrotation=0)
mpf.plot(goog, type='line', ax=ax2, axtitle='GOOG', xrotation=0)
mpf.plot(tsla, type='line', ax=ax3, axtitle='TSLA', xrotation=0)
ax1.set_xticklabels([])
ax2.set_xticklabels([])
matplotlib
N = len(tickers)
fig, axes = plt.subplots(N, figsize=(20, 5*N), sharex=True)
for df,t,ax in zip([aapl,goog,tsla], tickers, axes):
mpf.plot(df, ax=ax, axtitle=t, type='line', show_nontrading=True)# volume=True
In addition to the techniques mentioned by #r-beginners there is another technique that may work for you in the case where all plots share the same x-axis. That is to use mpf.make_addplot().
aps = []
for coin in coins[1:]:
aps.append(mpf.make_addplot(df_coins[coin]['Close'], title=coin, type='line'))
coin = coins[0]
mpf.plot(df_coins[coin],axtitle=coin,type='line',volume=True,show_nontrading=True,addplot=aps)
If you choose to do type='candle' instead of 'line', then change
df_coins[coin]['Close']
to simply
df_coins[coin]
I've spent entirely too long researching how to get two subplots to share the same y-axis with a single colorbar shared between the two in Matplotlib.
What was happening was that when I called the colorbar() function in either subplot1 or subplot2, it would autoscale the plot such that the colorbar plus the plot would fit inside the 'subplot' bounding box, causing the two side-by-side plots to be two very different sizes.
To get around this, I tried to create a third subplot which I then hacked to render no plot with just a colorbar present.
The only problem is, now the heights and widths of the two plots are uneven, and I can't figure out how to make it look okay.
Here is my code:
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches
from matplotlib.ticker import NullFormatter
# SIS Functions
TE = 1 # Einstein radius
g1 = lambda x,y: (TE/2) * (y**2-x**2)/((x**2+y**2)**(3/2))
g2 = lambda x,y: -1*TE*x*y / ((x**2+y**2)**(3/2))
kappa = lambda x,y: TE / (2*np.sqrt(x**2+y**2))
coords = np.linspace(-2,2,400)
X,Y = np.meshgrid(coords,coords)
g1out = g1(X,Y)
g2out = g2(X,Y)
kappaout = kappa(X,Y)
for i in range(len(coords)):
for j in range(len(coords)):
if np.sqrt(coords[i]**2+coords[j]**2) <= TE:
g1out[i][j]=0
g2out[i][j]=0
fig = plt.figure()
fig.subplots_adjust(wspace=0,hspace=0)
# subplot number 1
ax1 = fig.add_subplot(1,2,1,aspect='equal',xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{1}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
plt.ylabel(r"y ($\theta_{E}$)",rotation='horizontal',fontsize="15")
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.imshow(g1out,extent=(-2,2,-2,2))
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
e1 = patches.Ellipse((0,0),2,2,color='white')
ax1.add_patch(e1)
# subplot number 2
ax2 = fig.add_subplot(1,2,2,sharey=ax1,xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{2}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
ax2.yaxis.set_major_formatter( NullFormatter() )
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
plt.imshow(g2out,extent=(-2,2,-2,2))
e2 = patches.Ellipse((0,0),2,2,color='white')
ax2.add_patch(e2)
# subplot for colorbar
ax3 = fig.add_subplot(1,1,1)
ax3.axis('off')
cbar = plt.colorbar(ax=ax2)
plt.show()
Just place the colorbar in its own axis and use subplots_adjust to make room for it.
As a quick example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
plt.show()
Note that the color range will be set by the last image plotted (that gave rise to im) even if the range of values is set by vmin and vmax. If another plot has, for example, a higher max value, points with higher values than the max of im will show in uniform color.
You can simplify Joe Kington's code using the axparameter of figure.colorbar() with a list of axes.
From the documentation:
ax
None | parent axes object(s) from which space for a new colorbar axes will be stolen. If a list of axes is given they will all be resized to make room for the colorbar axes.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
This solution does not require manual tweaking of axes locations or colorbar size, works with multi-row and single-row layouts, and works with tight_layout(). It is adapted from a gallery example, using ImageGrid from matplotlib's AxesGrid Toolbox.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
# Set up figure and image grid
fig = plt.figure(figsize=(9.75, 3))
grid = ImageGrid(fig, 111, # as in plt.subplot(111)
nrows_ncols=(1,3),
axes_pad=0.15,
share_all=True,
cbar_location="right",
cbar_mode="single",
cbar_size="7%",
cbar_pad=0.15,
)
# Add data to image grid
for ax in grid:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
# Colorbar
ax.cax.colorbar(im)
ax.cax.toggle_label(True)
#plt.tight_layout() # Works, but may still require rect paramater to keep colorbar labels visible
plt.show()
Using make_axes is even easier and gives a better result. It also provides possibilities to customise the positioning of the colorbar.
Also note the option of subplots to share x and y axes.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
cax,kw = mpl.colorbar.make_axes([ax for ax in axes.flat])
plt.colorbar(im, cax=cax, **kw)
plt.show()
As a beginner who stumbled across this thread, I'd like to add a python-for-dummies adaptation of abevieiramota's very neat answer (because I'm at the level that I had to look up 'ravel' to work out what their code was doing):
import numpy as np
import matplotlib.pyplot as plt
fig, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3)
axlist = [ax1,ax2,ax3,ax4,ax5,ax6]
first = ax1.imshow(np.random.random((10,10)), vmin=0, vmax=1)
third = ax3.imshow(np.random.random((12,12)), vmin=0, vmax=1)
fig.colorbar(first, ax=axlist)
plt.show()
Much less pythonic, much easier for noobs like me to see what's actually happening here.
Shared colormap and colorbar
This is for the more complex case where the values are not just between 0 and 1; the cmap needs to be shared instead of just using the last one.
import numpy as np
from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig, axes = plt.subplots(nrows=2, ncols=2)
cmap=cm.get_cmap('viridis')
normalizer=Normalize(0,4)
im=cm.ScalarMappable(norm=normalizer)
for i,ax in enumerate(axes.flat):
ax.imshow(i+np.random.random((10,10)),cmap=cmap,norm=normalizer)
ax.set_title(str(i))
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
As pointed out in other answers, the idea is usually to define an axes for the colorbar to reside in. There are various ways of doing so; one that hasn't been mentionned yet would be to directly specify the colorbar axes at subplot creation with plt.subplots(). The advantage is that the axes position does not need to be manually set and in all cases with automatic aspect the colorbar will be exactly the same height as the subplots. Even in many cases where images are used the result will be satisfying as shown below.
When using plt.subplots(), the use of gridspec_kw argument allows to make the colorbar axes much smaller than the other axes.
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
Example:
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,8), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,8), vmin=0, vmax=1)
ax.set_ylabel("y label")
fig.colorbar(im, cax=cax)
plt.show()
This works well, if the plots' aspect is autoscaled or the images are shrunk due to their aspect in the width direction (as in the above). If, however, the images are wider then high, the result would look as follows, which might be undesired.
A solution to fix the colorbar height to the subplot height would be to use mpl_toolkits.axes_grid1.inset_locator.InsetPosition to set the colorbar axes relative to the image subplot axes.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(7,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,16), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,16), vmin=0, vmax=1)
ax.set_ylabel("y label")
ip = InsetPosition(ax2, [1.05,0,0.05,1])
cax.set_axes_locator(ip)
fig.colorbar(im, cax=cax, ax=[ax,ax2])
plt.show()
New in matplotlib 3.4.0
Shared colorbars can now be implemented using subfigures:
New Figure.subfigures and Figure.add_subfigure allow ... localized figure artists (e.g., colorbars and suptitles) that only pertain to each subfigure.
The matplotlib gallery includes demos on how to plot subfigures.
Here is a minimal example with 2 subfigures, each with a shared colorbar:
fig = plt.figure(constrained_layout=True)
(subfig_l, subfig_r) = fig.subfigures(nrows=1, ncols=2)
axes_l = subfig_l.subplots(nrows=1, ncols=2, sharey=True)
for ax in axes_l:
im = ax.imshow(np.random.random((10, 10)), vmin=0, vmax=1)
# shared colorbar for left subfigure
subfig_l.colorbar(im, ax=axes_l, location='bottom')
axes_r = subfig_r.subplots(nrows=3, ncols=1, sharex=True)
for ax in axes_r:
mesh = ax.pcolormesh(np.random.randn(30, 30), vmin=-2.5, vmax=2.5)
# shared colorbar for right subfigure
subfig_r.colorbar(mesh, ax=axes_r)
The solution of using a list of axes by abevieiramota works very well until you use only one row of images, as pointed out in the comments. Using a reasonable aspect ratio for figsize helps, but is still far from perfect. For example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(9.75, 3))
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
The colorbar function provides the shrink parameter which is a scaling factor for the size of the colorbar axes. It does require some manual trial and error. For example:
fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.75)
To add to #abevieiramota's excellent answer, you can get the euqivalent of tight_layout with constrained_layout. You will still get large horizontal gaps if you use imshow instead of pcolormesh because of the 1:1 aspect ratio imposed by imshow.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
for ax in axes.flat:
im = ax.pcolormesh(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.flat)
plt.show()
I noticed that almost every solution posted involved ax.imshow(im, ...) and did not normalize the colors displayed to the colorbar for the multiple subfigures. The im mappable is taken from the last instance, but what if the values of the multiple im-s are different? (I'm assuming these mappables are treated in the same way that the contour-sets and surface-sets are treated.) I have an example using a 3d surface plot below that creates two colorbars for a 2x2 subplot (one colorbar per one row). Although the question asks explicitly for a different arrangement, I think the example helps clarify some things. I haven't found a way to do this using plt.subplots(...) yet because of the 3D axes unfortunately.
If only I could position the colorbars in a better way... (There is probably a much better way to do this, but at least it should be not too difficult to follow.)
import matplotlib
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
cmap = 'plasma'
ncontours = 5
def get_data(row, col):
""" get X, Y, Z, and plot number of subplot
Z > 0 for top row, Z < 0 for bottom row """
if row == 0:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 1
else:
pnum = 2
elif row == 1:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = -np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 3
else:
pnum = 4
print("\nPNUM: {}, Zmin = {}, Zmax = {}\n".format(pnum, np.min(Z), np.max(Z)))
return X, Y, Z, pnum
fig = plt.figure()
nrows, ncols = 2, 2
zz = []
axes = []
for row in range(nrows):
for col in range(ncols):
X, Y, Z, pnum = get_data(row, col)
ax = fig.add_subplot(nrows, ncols, pnum, projection='3d')
ax.set_title('row = {}, col = {}'.format(row, col))
fhandle = ax.plot_surface(X, Y, Z, cmap=cmap)
zz.append(Z)
axes.append(ax)
## get full range of Z data as flat list for top and bottom rows
zz_top = zz[0].reshape(-1).tolist() + zz[1].reshape(-1).tolist()
zz_btm = zz[2].reshape(-1).tolist() + zz[3].reshape(-1).tolist()
## get top and bottom axes
ax_top = [axes[0], axes[1]]
ax_btm = [axes[2], axes[3]]
## normalize colors to minimum and maximum values of dataset
norm_top = matplotlib.colors.Normalize(vmin=min(zz_top), vmax=max(zz_top))
norm_btm = matplotlib.colors.Normalize(vmin=min(zz_btm), vmax=max(zz_btm))
cmap = cm.get_cmap(cmap, ncontours) # number of colors on colorbar
mtop = cm.ScalarMappable(cmap=cmap, norm=norm_top)
mbtm = cm.ScalarMappable(cmap=cmap, norm=norm_btm)
for m in (mtop, mbtm):
m.set_array([])
# ## create cax to draw colorbar in
# cax_top = fig.add_axes([0.9, 0.55, 0.05, 0.4])
# cax_btm = fig.add_axes([0.9, 0.05, 0.05, 0.4])
cbar_top = fig.colorbar(mtop, ax=ax_top, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_top)
cbar_top.set_ticks(np.linspace(min(zz_top), max(zz_top), ncontours))
cbar_btm = fig.colorbar(mbtm, ax=ax_btm, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_btm)
cbar_btm.set_ticks(np.linspace(min(zz_btm), max(zz_btm), ncontours))
plt.show()
plt.close(fig)
## orientation of colorbar = 'horizontal' if done by column
This topic is well covered but I still would like to propose another approach in a slightly different philosophy.
It is a bit more complex to set-up but it allow (in my opinion) a bit more flexibility. For example, one can play with the respective ratios of each subplots / colorbar:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec
# Define number of rows and columns you want in your figure
nrow = 2
ncol = 3
# Make a new figure
fig = plt.figure(constrained_layout=True)
# Design your figure properties
widths = [3,4,5,1]
gs = GridSpec(nrow, ncol + 1, figure=fig, width_ratios=widths)
# Fill your figure with desired plots
axes = []
for i in range(nrow):
for j in range(ncol):
axes.append(fig.add_subplot(gs[i, j]))
im = axes[-1].pcolormesh(np.random.random((10,10)))
# Shared colorbar
axes.append(fig.add_subplot(gs[:, ncol]))
fig.colorbar(im, cax=axes[-1])
plt.show()
The answers above are great, but most of them use the fig.colobar() method applied to a fig object. This example shows how to use the plt.colobar() function, applied directly to pyplot:
def shared_colorbar_example():
fig, axs = plt.subplots(nrows=3, ncols=3)
for ax in axs.flat:
plt.sca(ax)
color = np.random.random((10))
plt.scatter(range(10), range(10), c=color, cmap='viridis', vmin=0, vmax=1)
plt.colorbar(ax=axs.ravel().tolist(), shrink=0.6)
plt.show()
shared_colorbar_example()
Since most answers above demonstrated usage on 2D matrices, I went with a simple scatter plot. The shrink keyword is optional and resizes the colorbar.
If vmin and vmax are not specified this approach will automatically analyze all of the subplots for the minimum and maximum value to be used on the colorbar. The above approaches when using fig.colorbar(im) scan only the image passed as argument for min and max values of the colorbar.
Result: