Develop Reference

Remove default axis labels in matplotlib subplots

How do I prevent matplotlib from adding default axis labels to my subplot? The simple code below also labels the x and y axes for 0 to 1. This is messing up my plot when I add actual data (the new labels are added according to the data but the 0 to 1 labels are still present).
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
ax1 = fig.add_subplot(111)
plt.show(block=True)
The above produces this figure:
So when I include actual data it looks like this, with just this additional line of code (final_wavelengths and final_stellar are numpy arrays of equal length):
ax1.plot(final_wavelengths, final_stellar, 'b', label='stellar')
How can I remove the unwanted default 0-1 labels?

I was pointed to similar issue. for me worked explicit assigning to right axes by using ax = fig.axes[0] (I had a list of 2 different axes and needed for work just the first one)

Matplotlib delete plot stribes

I have the following issue displayed in the image below:
For an improved clarity I want do delete the stripes on the x axis or put them below the x axis. (Also it would be nice If you know a solution to the problem of overlapping numbers)

Assuming you have defined your plot and axes as below:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
If you want to remove the x axis tick marks you can do:
ax.tick_params(axis='x', top='off', bottom='off')
If you want to change the direction of the tick marks you can do:
ax.tick_params(axis='x', direction='out')
If you want to change the x axis labels then use:
set_xticklabels()
You have to pass a list of labels to use, although I'm not sure why your labels aren't evenly spaced. The documentation at the link below should help:
matplotlib.axes documentation

Matplotlib - axvspan vs subplots

I'm writing a pythonic script for a coastal engineering application which should output, amongst other things, a figure with two subplots.
The problem is that I would like to shade a section of both subplots using plt.axvspan() but for some reason it only shades one of them.
Please find below an excerpt of the section of the code where I set up the plots as well as the figure that it's currently outputting (link after code).
Thanks for your help, and sorry if this is a rookie question (but it just happens that I am indeed a rookie in Python... and programming in general) but I couldn't find an answer for this anywhere else.
Feel free to add any comments to the code.
# PLOTTING
# now we generate a figure with the bathymetry vs required m50 and another figure with bathy vs Hs
#1. Generate plots
fig = plt.figure() # Generate Figure
ax = fig.add_subplot(211) # add the first plot to the figure.
depth = ax.plot(results[:,0],results[:,1]*-1,label="Depth [mDMD]") #plot the first set of data onto the first set of axis.
ax2 = ax.twinx() # generate a secondary vertical axis with the same horizontal axis as the first
m50 = ax2.plot(results[:,0],results[:,6],"r",label="M50 [kg]") # plot the second set of data onto the second vertical axis
ax3 = fig.add_subplot(212) # generate the second subplot
hs = ax3.plot(results[:,0],results[:,2],"g",label="Hs(m)")
#Now we want to find where breaking starts to occur so we shade it on the plot.
xBreakingDistance = results[numpy.argmax(breakingIndex),0]
# and now we plot a box from the origin to the depth of breaking.
plt.axvspan(0,xBreakingDistance,facecolor="b",alpha=0.1) # this box is called a span in matplotlib (also works for axhspan)
# and then we write BREAKING ZONE in the box we just created
yLimits = ax.get_ylim() # first we get the range of y being plotted
yMiddle = (float(yLimits[1])-float(yLimits[0])) / 2 + yLimits[0] # then we calculate the middle value in y (to center the text)
xMiddle = xBreakingDistance / 2 # and then the middle value in x (to center the text)
#now we write BREAKING ZONE in the center of the box.
ax.text(xMiddle,yMiddle,"BREAKING ZONE",fontweight="bold",rotation=90,verticalalignment="center",horizontalalignment="center")
#FIGURE FORMATTING
ax.set_xlabel("Distance [m]") # define x label
ax.set_ylabel("Depth [mDMD]") # define y label on the first vertical axis (ax)
ax2.set_ylabel("M50 [kg]") # define y label on the second vertical axis (ax2)
ax.grid() # show grid
ax3.set_xlabel("Distance[m]") #define x label
ax3.set_ylabel("Hs[m]") # define y label
ax3.grid()
plt.tight_layout() # minimize subplot labels overlapping
# generating a label on a plot with 2 vertical axis is not very intuitive. Normally we would just write ax.label(loc=0)
combined_plots = depth+m50 #first we need to combine the plots in a vector
combined_labels = [i.get_label() for i in combined_plots] # and then we combine the labels
ax.legend(combined_plots,combined_labels,loc=0) # and finally we plot the combined_labels of the combined_plots
plt.savefig("Required M50(kg) along the trench.png",dpi=1000)
plt.close(fig)
Output Figure:

By just calling plt.axvspan, you are telling matplotlib to create the axvspan on the currently active axes (i.e. in this case, the last one you created, ax3)
You need to plot the axvspan on both of the axes you would like for it to appear on. In this case, ax and ax3.
So, you could do:
ax.axvspan(0,xBreakingDistance,facecolor="b",alpha=0.1)
ax3.axvspan(0,xBreakingDistance,facecolor="b",alpha=0.1)
or in one line:
[this_ax.axvspan(0,xBreakingDistance,facecolor="b",alpha=0.1) for this_ax in [ax,ax3]]

It's difficult to analyze your code and not being able to reproduce it. I advise you to build a minimal example. In any case notice that you are calling "plt.axvspan(" which is general call to the library.
You need to specifically state that you want this in both "ax" and "ax2" (i think).
Also if you need more control consider using Patches (I don't know axvspan):
import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, aspect='equal')
ax1.add_patch(
patches.Rectangle(
(0.1, 0.1), # (x,y)
0.5, # width
0.5, # height
)
)
fig1.savefig('rect1.png', dpi=90, bbox_inches='tight')
See that call to "ax1" in the example? Just make something similar to yours. Or just add axvspan to each of your plots.

Change distance between boxplots in the same figure in python [duplicate]

I'm drawing the bloxplot shown below using python and matplotlib. Is there any way I can reduce the distance between the two boxplots on the X axis?
This is the code that I'm using to get the figure above:
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['ytick.direction'] = 'out'
rcParams['xtick.direction'] = 'out'
fig = plt.figure()
xlabels = ["CG", "EG"]
ax = fig.add_subplot(111)
ax.boxplot([values_cg, values_eg])
ax.set_xticks(np.arange(len(xlabels))+1)
ax.set_xticklabels(xlabels, rotation=45, ha='right')
fig.subplots_adjust(bottom=0.3)
ylabels = yticks = np.linspace(0, 20, 5)
ax.set_yticks(yticks)
ax.set_yticklabels(ylabels)
ax.tick_params(axis='x', pad=10)
ax.tick_params(axis='y', pad=10)
plt.savefig(os.path.join(output_dir, "output.pdf"))
And this is an example closer to what I'd like to get visually (although I wouldn't mind if the boxplots were even a bit closer to each other):

You can either change the aspect ratio of plot or use the widths kwarg (doc) as such:
ax.boxplot([values_cg, values_eg], widths=1)
to make the boxes wider.

Try changing the aspect ratio using
ax.set_aspect(1.5) # or some other float
The larger then number, the narrower (and taller) the plot should be:
a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect=’equal’.
http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_aspect

When your code writes:
ax.set_xticks(np.arange(len(xlabels))+1)
You're putting the first box plot on 0 and the second one on 1 (event though you change the tick labels afterwards), just like in the second, "wanted" example you gave they are set on 1,2,3.
So i think an alternative solution would be to play with the xticks position and the xlim of the plot.
for example using
ax.set_xlim(-1.5,2.5)
would place them closer.

positions : array-like, optional
Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to range(1, N+1) where N is the number of boxes to be drawn.
https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.boxplot.html
This should do the job!

As #Stevie mentioned, you can use the positions kwarg (doc) to manually set the x-coordinates of the boxes:
ax.boxplot([values_cg, values_eg], positions=[1, 1.3])

rotating xticks causes the ticks partially hidden in matplotlib

I am creating a plot with names on x axis and time values(minutes) on y axis.The names on x axis are like
['cooking']18:15:27 ,['study']18:09:19,['travel']18:21:34` etc ..
where as the y values are 5,1,1 etc.I have given xlabel as 'categories' and ylabel as 'durations in minutes'.
Since the xticks were strings of some length,I decided to rotate them by 90 to avoid overlapping.Now ,the ticks are partially hidden and the xlabel has disappeared.
Is there some way I can make the whole plot accommodate everything..?
thanks
mark
here is the code snippet
import matplotlib.pyplot as plt
...
figure = plt.figure()
barwidth = 0.25
ystep = 10
plt.grid(True)
plt.xlabel('categories')
plt.ylabel('durations in minutes')
plt.title('durations for categories-created at :'+now)
plt.bar(xdata, ydata, width=barwidth,align='center')
plt.xticks(xdata,catnames,rotation=90)
plt.yticks(range(0,maxduration+ystep,ystep))
plt.xlim([min(xdata) - 0.5, max(xdata) + 0.5])
plt.ylim(0,max(ydata)+ystep)
figure.savefig("myplot.png",format="png")

plt.tight_layout()
But be sure to add this command after plt.plot() or plt.bar()

One good option is to rotate the tick labels.
In your specific case, you might find it convenient to use figure.autofmt_xdate() (Which will rotate the x-axis labels among other things).
Alternatively, you could do plt.setp(plt.xticks()[1], rotation=30) (or various other ways of doing the same thing).
Also, as a several year later edit, with recent versions of matplotlib, you can call fig.tight_layout() to resize things to fit the labels inside the figure, as #elgehelge notes below.

Setting the bounding box when saving will also show the labels:
figure.savefig('myplot.png', bbox_inches='tight')