I have two lists, x_axis which is list of time in the format of '12:30:00'. The y-axis is percent values. I need to plot all the values on a graph, however since x-axis string is too long they overlap. Is there anyway I can have matplotlib not show every single time on x-axis? Any help would be appreciated.
You could rotate and print every 2nd ticklabel:
_ = plt.plot(df['str_time'], df.Pct, 'ro')
ax = plt.gca()
plt.axis([0,24,0,50])
plt.xticks(rotation=90)
for label in ax.get_xaxis().get_ticklabels()[::2]:
label.set_visible(False)
Output:
You can rotate your label to show the list time using the below code.
plt.xticks(rotation=90)
One way to do this automatically is by using autofmt_xdate
fig.autofmt_xdate():
for getting fig object you will have to call the subplot functions
fig, ax = plt.subplots()
Works really well
I needed to step x axis digits instead of rotating.
ax.set_xticks(np.arange(0, max_number, 5)) #step 5 digits
Output:
Related
I am having an issue trying to get my date ticks rotated in matplotlib. A small sample program is below. If I try to rotate the ticks at the end, the ticks do not get rotated. If I try to rotate the ticks as shown under the comment 'crashes', then matplot lib crashes.
This only happens if the x-values are dates. If I replaces the variable dates with the variable t in the call to avail_plot, the xticks(rotation=70) call works just fine inside avail_plot.
Any ideas?
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
def avail_plot(ax, x, y, label, lcolor):
ax.plot(x,y,'b')
ax.set_ylabel(label, rotation='horizontal', color=lcolor)
ax.get_yaxis().set_ticks([])
#crashes
#plt.xticks(rotation=70)
ax2 = ax.twinx()
ax2.plot(x, [1 for a in y], 'b')
ax2.get_yaxis().set_ticks([])
ax2.set_ylabel('testing')
f, axs = plt.subplots(2, sharex=True, sharey=True)
t = np.arange(0.01, 5, 1)
s1 = np.exp(t)
start = dt.datetime.now()
dates=[]
for val in t:
next_val = start + dt.timedelta(0,val)
dates.append(next_val)
start = next_val
avail_plot(axs[0], dates, s1, 'testing', 'green')
avail_plot(axs[1], dates, s1, 'testing2', 'red')
plt.subplots_adjust(hspace=0, bottom=0.3)
plt.yticks([0.5,],("",""))
#doesn't crash, but does not rotate the xticks
#plt.xticks(rotation=70)
plt.show()
If you prefer a non-object-oriented approach, move plt.xticks(rotation=70) to right before the two avail_plot calls, eg
plt.xticks(rotation=70)
avail_plot(axs[0], dates, s1, 'testing', 'green')
avail_plot(axs[1], dates, s1, 'testing2', 'red')
This sets the rotation property before setting up the labels. Since you have two axes here, plt.xticks gets confused after you've made the two plots. At the point when plt.xticks doesn't do anything, plt.gca() does not give you the axes you want to modify, and so plt.xticks, which acts on the current axes, is not going to work.
For an object-oriented approach not using plt.xticks, you can use
plt.setp( axs[1].xaxis.get_majorticklabels(), rotation=70 )
after the two avail_plot calls. This sets the rotation on the correct axes specifically.
Solution works for matplotlib 2.1+
There exists an axes method tick_params that can change tick properties. It also exists as an axis method as set_tick_params
ax.tick_params(axis='x', rotation=45)
Or
ax.xaxis.set_tick_params(rotation=45)
As a side note, the current solution mixes the stateful interface (using pyplot) with the object-oriented interface by using the command plt.xticks(rotation=70). Since the code in the question uses the object-oriented approach, it's best to stick to that approach throughout. The solution does give a good explicit solution with plt.setp( axs[1].xaxis.get_majorticklabels(), rotation=70 )
An easy solution which avoids looping over the ticklabes is to just use
fig.autofmt_xdate()
This command automatically rotates the xaxis labels and adjusts their position. The default values are a rotation angle 30° and horizontal alignment "right". But they can be changed in the function call
fig.autofmt_xdate(bottom=0.2, rotation=30, ha='right')
The additional bottom argument is equivalent to setting plt.subplots_adjust(bottom=bottom), which allows to set the bottom axes padding to a larger value to host the rotated ticklabels.
So basically here you have all the settings you need to have a nice date axis in a single command.
A good example can be found on the matplotlib page.
Another way to applyhorizontalalignment and rotation to each tick label is doing a for loop over the tick labels you want to change:
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
now = dt.datetime.now()
hours = [now + dt.timedelta(minutes=x) for x in range(0,24*60,10)]
days = [now + dt.timedelta(days=x) for x in np.arange(0,30,1/4.)]
hours_value = np.random.random(len(hours))
days_value = np.random.random(len(days))
fig, axs = plt.subplots(2)
fig.subplots_adjust(hspace=0.75)
axs[0].plot(hours,hours_value)
axs[1].plot(days,days_value)
for label in axs[0].get_xmajorticklabels() + axs[1].get_xmajorticklabels():
label.set_rotation(30)
label.set_horizontalalignment("right")
And here is an example if you want to control the location of major and minor ticks:
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
fig, axs = plt.subplots(2)
fig.subplots_adjust(hspace=0.75)
now = dt.datetime.now()
hours = [now + dt.timedelta(minutes=x) for x in range(0,24*60,10)]
days = [now + dt.timedelta(days=x) for x in np.arange(0,30,1/4.)]
axs[0].plot(hours,np.random.random(len(hours)))
x_major_lct = mpl.dates.AutoDateLocator(minticks=2,maxticks=10, interval_multiples=True)
x_minor_lct = matplotlib.dates.HourLocator(byhour = range(0,25,1))
x_fmt = matplotlib.dates.AutoDateFormatter(x_major_lct)
axs[0].xaxis.set_major_locator(x_major_lct)
axs[0].xaxis.set_minor_locator(x_minor_lct)
axs[0].xaxis.set_major_formatter(x_fmt)
axs[0].set_xlabel("minor ticks set to every hour, major ticks start with 00:00")
axs[1].plot(days,np.random.random(len(days)))
x_major_lct = mpl.dates.AutoDateLocator(minticks=2,maxticks=10, interval_multiples=True)
x_minor_lct = matplotlib.dates.DayLocator(bymonthday = range(0,32,1))
x_fmt = matplotlib.dates.AutoDateFormatter(x_major_lct)
axs[1].xaxis.set_major_locator(x_major_lct)
axs[1].xaxis.set_minor_locator(x_minor_lct)
axs[1].xaxis.set_major_formatter(x_fmt)
axs[1].set_xlabel("minor ticks set to every day, major ticks show first day of month")
for label in axs[0].get_xmajorticklabels() + axs[1].get_xmajorticklabels():
label.set_rotation(30)
label.set_horizontalalignment("right")
Simply use
ax.set_xticklabels(label_list, rotation=45)
I am clearly late but there is an official example which uses
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
to rotate the labels while keeping them correctly aligned with the ticks, which is both clean and easy.
Ref: https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html
I'm struggling to wrap my head around matplotlib with dataframes today. I see lots of solutions but I'm struggling to relate them to my needs. I think I may need to start over. Let's see what you think.
I have a dataframe (ephem) with 4 columns - Time, Date, Altitude & Azimuth.
I produce a scatter for alt & az using:
chart = plt.scatter(ephem.Azimuth, ephem.Altitude, marker='x', color='black', s=8)
What's the most efficient way to set the values in the Time column as the labels/ticks on the x axis?
So:
the scale/gridlines etc all remain the same
the chart still plots alt and az
the y axis ticks/labels remain as is
only the x axis ticks/labels are changed to the Time column.
Thanks
This isn't by any means the cleanest piece of code but the following works for me:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.scatter(ephem.Azimuth, ephem.Altitude, marker='x', color='black', s=8)
labels = list(ephem.Time)
ax.set_xticklabels(labels)
plt.show()
Here you will explicitly force the set_xticklabels to the dataframe Time column which you have.
In other words, you want to change the x-axis tick labels using a list of values.
labels = ephem.Time.tolist()
# make your plot and before calling plt.show()
# insert the following two lines
ax = plt.gca()
ax.set_xticklabels(labels = labels)
plt.show()
I can't figure out how to rotate the text on the X Axis. Its a time stamp, so as the number of samples increase, they get closer and closer until they overlap. I'd like to rotate the text 90 degrees so as the samples get closer together, they aren't overlapping.
Below is what I have, it works fine with the exception that I can't figure out how to rotate the X axis text.
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 8}
matplotlib.rc('font', **font)
values = open('stats.csv', 'r').readlines()
time = [datetime.datetime.fromtimestamp(float(i.split(',')[0].strip())) for i in values[1:]]
delay = [float(i.split(',')[1].strip()) for i in values[1:]]
plt.plot(time, delay)
plt.grid(b='on')
plt.savefig('test.png')
This works for me:
plt.xticks(rotation=90)
Many "correct" answers here but I'll add one more since I think some details are left out of several. The OP asked for 90 degree rotation but I'll change to 45 degrees because when you use an angle that isn't zero or 90, you should change the horizontal alignment as well; otherwise your labels will be off-center and a bit misleading (and I'm guessing many people who come here want to rotate axes to something other than 90).
Easiest / Least Code
Option 1
plt.xticks(rotation=45, ha='right')
As mentioned previously, that may not be desirable if you'd rather take the Object Oriented approach.
Option 2
Another fast way (it's intended for date objects but seems to work on any label; doubt this is recommended though):
fig.autofmt_xdate(rotation=45)
fig you would usually get from:
fig = plt.gcf()
fig = plt.figure()
fig, ax = plt.subplots()
fig = ax.figure
Object-Oriented / Dealing directly with ax
Option 3a
If you have the list of labels:
labels = ['One', 'Two', 'Three']
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(labels, rotation=45, ha='right')
In later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks([1, 2, 3], labels, rotation=45, ha='right')
Option 3b
If you want to get the list of labels from the current plot:
# Unfortunately you need to draw your figure first to assign the labels,
# otherwise get_xticklabels() will return empty strings.
plt.draw()
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
As above, in later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks(ax.get_xticks(), ax.get_xticklabels(), rotation=45, ha='right')
Option 4
Similar to above, but loop through manually instead.
for label in ax.get_xticklabels():
label.set_rotation(45)
label.set_ha('right')
Option 5
We still use pyplot (as plt) here but it's object-oriented because we're changing the property of a specific ax object.
plt.setp(ax.get_xticklabels(), rotation=45, ha='right')
Option 6
This option is simple, but AFAIK you can't set label horizontal align this way so another option might be better if your angle is not 90.
ax.tick_params(axis='x', labelrotation=45)
Edit:
There's discussion of this exact "bug" but a fix hasn't been released (as of 3.4.0):
https://github.com/matplotlib/matplotlib/issues/13774
Easy way
As described here, there is an existing method in the matplotlib.pyplot figure class that automatically rotates dates appropriately for you figure.
You can call it after you plot your data (i.e.ax.plot(dates,ydata) :
fig.autofmt_xdate()
If you need to format the labels further, checkout the above link.
Non-datetime objects
As per languitar's comment, the method I suggested for non-datetime xticks would not update correctly when zooming, etc. If it's not a datetime object used as your x-axis data, you should follow Tommy's answer:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
Try pyplot.setp. I think you could do something like this:
x = range(len(time))
plt.xticks(x, time)
locs, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.plot(x, delay)
Appart from
plt.xticks(rotation=90)
this is also possible:
plt.xticks(rotation='vertical')
I came up with a similar example. Again, the rotation keyword is.. well, it's key.
from pylab import *
fig = figure()
ax = fig.add_subplot(111)
ax.bar( [0,1,2], [1,3,5] )
ax.set_xticks( [ 0.5, 1.5, 2.5 ] )
ax.set_xticklabels( ['tom','dick','harry'], rotation=45 ) ;
If you want to apply rotation on the axes object, the easiest way is using tick_params. For example.
ax.tick_params(axis='x', labelrotation=90)
Matplotlib documentation reference here.
This is useful when you have an array of axes as returned by plt.subplots, and it is more convenient than using set_xticks because in that case you need to also set the tick labels, and also more convenient that those that iterate over the ticks (for obvious reasons)
If using plt:
plt.xticks(rotation=90)
In case of using pandas or seaborn to plot, assuming ax as axes for the plot:
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
Another way of doing the above:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
My answer is inspired by cjohnson318's answer, but I didn't want to supply a hardcoded list of labels; I wanted to rotate the existing labels:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
The simplest solution is to use:
plt.xticks(rotation=XX)
but also
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom=X.XX)
e.g for dates I used rotation=45 and bottom=0.20 but you can do some test for your data
import pylab as pl
pl.xticks(rotation = 90)
To rotate the x-axis label to 90 degrees
for tick in ax.get_xticklabels():
tick.set_rotation(45)
It will depend on what are you plotting.
import matplotlib.pyplot as plt
x=['long_text_for_a_label_a',
'long_text_for_a_label_b',
'long_text_for_a_label_c']
y=[1,2,3]
myplot = plt.plot(x,y)
for item in myplot.axes.get_xticklabels():
item.set_rotation(90)
For pandas and seaborn that give you an Axes object:
df = pd.DataFrame(x,y)
#pandas
myplot = df.plot.bar()
#seaborn
myplotsns =sns.barplot(y='0', x=df.index, data=df)
# you can get xticklabels without .axes cause the object are already a
# isntance of it
for item in myplot.get_xticklabels():
item.set_rotation(90)
If you need to rotate labels you may need change the font size too, you can use font_scale=1.0 to do that.
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])
Matplotlib newbie here.
I have the following code:
from pylab import figure, show
import numpy
fig = figure()
ax = fig.add_subplot(111)
plot_data=[1.7,1.7,1.7,1.54,1.52]
xdata = range(len(plot_data))
labels = ["2009-June","2009-Dec","2010-June","2010-Dec","2011-June"]
ax.plot(xdata,plot_data,"b-")
ax.set_xticks(range(len(labels)))
ax.set_xticklabels(labels)
ax.set_yticks([1.4,1.6,1.8])
fig.canvas.draw()
show()
When you run that code, the resulting chart has a run-in with the first tick label (2009-June) and the origin. How can I get the graph to move over to make that more readable? I tried to put dummy data in, but then Matplotlib (correctly) treats that as data.
add two limits to the x and y axes to shift the tick labels a bit.
# grow the y axis down by 0.05
ax.set_ylim(1.35, 1.8)
# expand the x axis by 0.5 at two ends
ax.set_xlim(-0.5, len(labels)-0.5)
the result is
Because tick labels are text objects you can change their alignment. However to get access to the text properties you need to go through the set_yticklabels function. So add the line:
ax.set_yticklabels([1.4,1.6,1.8],va="bottom")
after your set_yticks call. Alternatively if you go through the pylab library directly, instead of accessing the function through the axes object, you can just set that in one line:
pylab.yticks([1.4,1.6,1.8],va="bottom")
I suggest change Y axis limits:
ax.set_ylim([1.2, 1.8])