Here you have a working example: import random import numpy from matplotlib import pyplot x = [random.gauss(3,1) for _ in range(400)] y = [random.gauss(4,2) for _ in range(400)] bins = numpy.linspace(-10, 10, 100) pyplot.hist(x, bins, alpha=0.5, label=”x”) pyplot.hist(y, bins, alpha=0.5, label=”y”) pyplot.legend(loc=”upper right”) pyplot.show()
The main problem is that (on your system) matplotlib chooses an x-using backend by default. I just had the same problem on one of my servers. The solution for me was to add the following code in a place that gets read before any other pylab/matplotlib/pyplot import: import matplotlib # Force matplotlib to not use … Read more
Use the set_facecolor(color) method of the axes object, which you’ve created one of the following ways: You created a figure and axis/es together fig, ax = plt.subplots(nrows=1, ncols=1) You created a figure, then axis/es later fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # nrows, ncols, index You used the stateful API (if you’re doing … Read more
Another way is to use the subplots function and pass the width ratio with gridspec_kw matplotlib Tutorial: Customizing Figure Layouts Using GridSpec and Other Functions matplotlib.gridspec.GridSpec has available gridspect_kw options 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, … Read more
The standard colormaps also all have reversed versions. They have the same names with _r tacked on to the end. (Documentation here.)
There is a new API that makes this even simpler. plt.gca().invert_xaxis() and/or plt.gca().invert_yaxis()
Solution 1: is to install the GUI backend tk I found a solution to my problem (thanks to the help of ImportanceOfBeingErnest). All I had to do was to install tkinter through the Linux bash terminal using the following command: sudo apt-get install python3-tk instead of installing it with pip or directly in the virtual … Read more
I’m not aware of any plotting method which takes arrays or lists but you could use annotate() while iterating over the values in n. import matplotlib.pyplot as plt y = [2.56422, 3.77284, 3.52623, 3.51468, 3.02199] z = [0.15, 0.3, 0.45, 0.6, 0.75] n = [58, 651, 393, 203, 123] fig, ax = plt.subplots() ax.scatter(z, y) … Read more
The default figure size (in inches) is controlled by matplotlib.rcParams[‘figure.figsize’] = [width, height] For example: import matplotlib.pyplot as plt plt.rcParams[‘figure.figsize’] = [10, 5] creates a figure with 10 (width) x 5 (height) inches
ax.title.set_text(‘My Plot Title’) seems to work too. fig = plt.figure() ax1 = fig.add_subplot(221) ax2 = fig.add_subplot(222) ax3 = fig.add_subplot(223) ax4 = fig.add_subplot(224) ax1.title.set_text(‘First Plot’) ax2.title.set_text(‘Second Plot’) ax3.title.set_text(‘Third Plot’) ax4.title.set_text(‘Fourth Plot’) plt.show()