The df.plot() function returns a matplotlib.axes.AxesSubplot object. You can set the labels on that object. ax = df2.plot(lw=2, colormap=’jet’, marker=”.”, markersize=10, title=”Video streaming dropout by category”) ax.set_xlabel(“x label”) ax.set_ylabel(“y label”) Or, more succinctly: ax.set(xlabel=”x label”, ylabel=”y label”). Alternatively, the index x-axis label is automatically set to the Index name, if it has one. so df2.index.name=”x … Read more
You can create a big subplot that covers the two subplots and then set the common labels. import random import matplotlib.pyplot as plt x = range(1, 101) y1 = [random.randint(1, 100) for _ in range(len(x))] y2 = [random.randint(1, 100) for _ in range(len(x))] fig = plt.figure() ax = fig.add_subplot(111) # The big subplot ax1 = … Read more
Sorry EMS, but I actually just got another response from the matplotlib mailling list (Thanks goes out to Benjamin Root). The code I am looking for is adjusting the savefig call to: fig.savefig(‘samplefigure’, bbox_extra_artists=(lgd,), bbox_inches=”tight”) #Note that the bbox_extra_artists must be an iterable This is apparently similar to calling tight_layout, but instead you allow savefig … Read more
As of matplotlib v1.4.0rc4, a remove method has been added to the legend object. Usage: ax.get_legend().remove() or legend = ax.legend(…) … legend.remove() See here for the commit where this was introduced.
Use axhline (a horizontal axis line). For example, this plots a horizontal line at y = 0.5: import matplotlib.pyplot as plt plt.axhline(y=0.5, color=”r”, linestyle=”-“) plt.show()
There is also a nice function get_legend_handles_labels() you can call on the last axis (if you iterate over them) that would collect everything you need from label= arguments: handles, labels = ax.get_legend_handles_labels() fig.legend(handles, labels, loc=”upper center”)
You can remove the white space padding by setting bbox_inches=”tight” in savefig: plt.savefig(“test.png”,bbox_inches=”tight”) You’ll have to put the argument to bbox_inches as a string, perhaps this is why it didn’t work earlier for you. Possible duplicates: Matplotlib plots: removing axis, legends and white spaces How to set the margins for a matplotlib figure? Reduce left … Read more
You have pylab.ylim: pylab.ylim([0,1000]) Note: The command has to be executed after the plot! Update 2021 Since the use of pylab is now strongly discouraged by matplotlib, you should instead use pyplot: from matplotlib import pyplot as plt plt.ylim(0, 100) #corresponding function for the x-axis plt.xlim(1, 1000)
Plots created using seaborn need to be displayed like ordinary matplotlib plots. This can be done using the plt.show() function from matplotlib. Originally I posted the solution to use the already imported matplotlib object from seaborn (sns.plt.show()) however this is considered to be a bad practice. Therefore, simply directly import the matplotlib.pyplot module and show … Read more
How about doing it with Pillow: from PIL import Image img = Image.open(‘image.png’).convert(‘L’) img.save(‘greyscale.png’) If an alpha (transparency) channel is present in the input image and should be preserved, use mode LA: img = Image.open(‘image.png’).convert(‘LA’) Using matplotlib and the formula Y’ = 0.2989 R + 0.5870 G + 0.1140 B you could do: import numpy … Read more