How can I prevent the labels of xticks from overlapping with the labels of yticks when using hist (or other plotting commands) in matplotlib?
There are several ways.
One is to use the tight_layout method of the figure you are drawing, which will automatically try to optimize the appareance of the labels.
fig, ax = subplots(1)
ax.plot(arange(10),rand(10))
fig.tight_layout()
An other way is to modify the rcParams values for the ticks formatting:
rcParams['xtick.major.pad'] = 6
This will draw the ticks a little farter from the axes. after modifying the rcparams (this of any other, you can find the complete list on your matplotlibrc configuration file), remember to set it back to deafult with the rcdefaults function.
A third way is to tamper with the axes locator_params telling it to not draw the label in the corner:
fig, ax = subplots(1)
ax.plot(arange(10),rand(10))
ax.locator_params(prune='lower',axis='both')
the axis keywords tell the locator on which axis it should work and the prune keyword tell it to remove the lowest value of the tick
Try increasing the padding between the ticks on the labels
import matplotlib
matplotlib.rcParams['xtick.major.pad'] = 8 # defaults are 4
matplotlib.rcParams['ytick.major.pad'] = 8
same goes for [x|y]tick.minor.pad.
Also, try setting: [x|y]tick.direction to 'out'. That gives you a little more room and helps makes the ticks a little more visible -- especially on histograms with dark bars.
Related
I need to precisely control the position of my ylabel independently of my yticklabels with matplotlib. This is because I have a matplotlib animation that currently has the ylabel jumping around as I change yticklabels. This is undesirable.
The docs seem to only allow me to specify distance from the leftmost part of my yticklabels. (which does not solve the problem, and indeed is causing it)
One solution would be to manually put the label. But is there a simpler way?
You can emulate the behavior of a normal y-label by adding text explicitly to the axes. If the y-limits are changing quite a bit, this is best done by placing the text in axes coordinates, rather than data coordinates. This is done with the transform keyword-argument, like so:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
t = ax.text(-0.1, 0.5, 'Y label', rotation=90,
verticalalignment='center', horizontalalignment='right',
transform=ax.transAxes)
ax.set_ylim(-10, 10) # Change y-limits, label position won't change.
This places the text halfway up the axes, and slightly to the left. Changes to the data limits of the axes have no effect on the text, as it is always defined in axes coordinates. Similarly, scaling the plot or axes (resizing the window with the mouse, using fig.set_size_inches, etc) will keep the y-label in position relative to the axes box itself, exactly what you want for a label.
You may have to play with the x-position of the label, to make sure it doesn't overlap the tickmarks as they change during animation.
I created a matplotlib plot that has 2 y-axes. The y-axes have different scales, but I want the ticks and grid to be aligned. I am pulling the data from excel files, so there is no way to know the max limits beforehand. I have tried the following code.
# creates double-y axis
ax2 = ax1.twinx()
locs = ax1.yaxis.get_ticklocs()
ax2.set_yticks(locs)
The problem now is that the ticks on ax2 do not have labels anymore. Can anyone give me a good way to align ticks with different scales?
Aligning the tick locations of two different scales would mean to give up on the nice automatic tick locator and set the ticks to the same positions on the secondary axes as on the original one.
The idea is to establish a relation between the two axes scales using a function and set the ticks of the second axes at the positions of those of the first.
import matplotlib.pyplot as plt
import matplotlib.ticker
fig, ax = plt.subplots()
# creates double-y axis
ax2 = ax.twinx()
ax.plot(range(5), [1,2,3,4,5])
ax2.plot(range(6), [13,17,14,13,16,12])
ax.grid()
l = ax.get_ylim()
l2 = ax2.get_ylim()
f = lambda x : l2[0]+(x-l[0])/(l[1]-l[0])*(l2[1]-l2[0])
ticks = f(ax.get_yticks())
ax2.yaxis.set_major_locator(matplotlib.ticker.FixedLocator(ticks))
plt.show()
Note that this is a solution for the general case and it might result in totally unreadable labels depeding on the use case. If you happen to have more a priori information on the axes range, better solutions may be possible.
Also see this question for a case where automatic tick locations of the first axes is sacrificed for an easier setting of the secondary axes tick locations.
To anyone who's wondering (and for my future reference), the lambda function f in ImportanceofBeingErnest's answer maps the input left tick to a corresponding right tick through:
RHS tick = Bottom RHS tick + (% of LHS range traversed * RHS range)
Refer to this question on tick formatting to truncate decimal places:
from matplotlib.ticker import FormatStrFormatter
ax2.yaxis.set_major_formatter(FormatStrFormatter('%.2f')) # ax2 is the RHS y-axis
I have this weird thing with the scale of the axis showing out of the figure like:
And what I want to have:
How can I move the scale to the other side of the axis?
x=range(len(ticks))
plt.plot(x,phase1,'r^-',label='$\Delta \phi(U1,I1)$')
plt.plot(x,phase2,'go-',label='$\Delta \phi(U2,I2)$')
plt.plot(x,phase3,'b*-',label='$\Delta \phi(U3,I3)$')
plt.xticks(x,ticks,rotation=45)
plt.xlabel('Messung')
plt.ylabel('$\Delta \phi [^\circ]$')
plt.legend()
plt.show()
The tick_params of your axis can be used to control axes label and ticks location. Set direction to in so that they point into the graph.
And here is a great example if you want different y-axis ranges and colours too.
from matplotlib import pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.tick_params(direction='in', length=6, width=2, colors='r', right=True, labelright='on')
plt.show()
You can use plt.tick_params() to adjust the behaviour of the ticks, documentation can be found here.
For your example you want the ticks to appear inside the figure. Therefore add
plt.tick_params(direction="in")
to your code. Example:
x=range(len(ticks))
plt.plot(x,phase1,'r^-',label='$\Delta \phi(U1,I1)$')
plt.plot(x,phase2,'go-',label='$\Delta \phi(U2,I2)$')
plt.plot(x,phase3,'b*-',label='$\Delta \phi(U3,I3)$')
plt.xticks(x,ticks,rotation=45)
plt.xlabel('Messung')
plt.ylabel('$\Delta \phi [^\circ]$')
plt.legend()
plt.tick_params(direction="in") # Set ticks inside the figure
plt.show()
You can get the ticks to appear on the top and right side of the figure too as shown in your second screenshot by adding:
plt.tick_params(direction="in",top="on",right="on")
If you wanted to make all figures in your Python script have this behaviour then you can add the following at the top of your script (this might be of interest):
import matplotlib
matplotlib.rcParams['xtick.direction'] = "in"
matplotlib.rcParams['ytick.direction'] = "in"
This will save you having to call plt.tick_params() for each figure, which is helpful if you generate lots of figures.
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])
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')