Actually, it’s quite easy: instead of the number of bins you can give a list with the bin boundaries. They can be unequally distributed, too: plt.hist(data, bins=[0, 10, 20, 30, 40, 50, 100]) If you just want them equally distributed, you can simply use range: plt.hist(data, bins=range(min(data), max(data) + binwidth, binwidth)) Added to original answer … Read more
Ignoring the smooth interpolation between points in your example graph (that would require doing some manual interpolation, or just have a higher resolution of your data), you can use pyplot.fill_between(): from matplotlib import pyplot as plt import numpy as np x = np.linspace(0, 30, 30) y = np.sin(x/6*np.pi) error = np.random.normal(0.1, 0.02, size=y.shape) y += … Read more
There’s no need to manually set the colors. Instead, specify a grayscale colormap… import numpy as np import matplotlib.pyplot as plt # Generate data… x = np.random.random(10) y = np.random.random(10) # Plot… plt.scatter(x, y, c=y, s=500) plt.gray() plt.show() Or, if you’d prefer a wider range of colormaps, you can also specify the cmap kwarg to … Read more
@Neil’s answer is one (perfectly valid!) way of doing it, but you can also simply call matplotlib.use(‘Agg’) before importing matplotlib.pyplot, and then continue as normal. E.g. import matplotlib as mpl mpl.use(‘Agg’) import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) ax.plot(range(10)) fig.savefig(‘temp.png’) You don’t have to use the Agg backend, as well. The pdf, … Read more
Using vmin and vmax forces the range for the colors. Here’s an example: import matplotlib as m import matplotlib.pyplot as plt import numpy as np cdict = { ‘red’ : ( (0.0, 0.25, .25), (0.02, .59, .59), (1., 1., 1.)), ‘green’: ( (0.0, 0.0, 0.0), (0.02, .45, .45), (1., .97, .97)), ‘blue’ : ( (0.0, … Read more
You can set the horizontal alignment of ticklabels, see the example below. If you imagine a rectangular box around the rotated label, which side of the rectangle do you want to be aligned with the tickpoint? Given your description, you want: ha=”right” n=5 x = np.arange(n) y = np.sin(np.linspace(-3,3,n)) xlabels = [‘Ticklabel %i’ % i … Read more
The best way is to interact with the axes object directly import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 10, 0.1) y1 = 0.05 * x**2 y2 = -1 *y1 fig, ax1 = plt.subplots() ax2 = ax1.twinx() ax1.plot(x, y1, ‘g-‘) ax2.plot(x, y2, ‘b-‘) ax1.set_xlabel(‘X data’) ax1.set_ylabel(‘Y1 data’, color=”g”) ax2.set_ylabel(‘Y2 data’, color=”b”) plt.show()
If I set my backend to template in ~/.matplotlib/matplotlibrc, then I can reproduce your symptoms: ~/.matplotlib/matplotlibrc: # backend : GtkAgg backend : template Note that the file matplotlibrc may not be in directory ~/.matplotlib/. In this case, the following code shows where it is: >>> import matplotlib >>> matplotlib.matplotlib_fname() In [1]: import matplotlib.pyplot as p … Read more
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) … Read more
There is a clear figure command, and it should do it for you: plt.clf() If you have multiple subplots in the same figure plt.cla() clears the current axes.