Solving problem is about exposing yourself to as many situations as possible like Modify tick label text and practice these strategies over and over. With time, it becomes second nature and a natural way you approach any problems in general. Big or small, always start with a plan, use other strategies mentioned here till you are confident and ready to code the solution.
In this post, my aim is to share an overview the topic about Modify tick label text, which can be followed any time. Take easy to follow this discuss.
I want to make some modifications to a few selected tick labels in a plot.
For example, if I do:
label = axes.yaxis.get_major_ticks().label label.set_fontsize(size) label.set_rotation('vertical')
the font size and the orientation of the tick label is changed.
However, if try:
the tick label is not modified. Also if I do:
nothing is printed.
Here’s some more strangeness. When I tried this:
from pylab import * axes = figure().add_subplot(111) t = arange(0.0, 2.0, 0.01) s = sin(2*pi*t) axes.plot(t, s) for ticklabel in axes.get_xticklabels(): print ticklabel.get_text()
Only empty strings are printed, but the plot contains ticks labeled as ‘0.0’, ‘0.5’, ‘1.0’, ‘1.5’, and ‘2.0’.
Caveat: Unless the ticklabels are already set to a string (as is usually the case in e.g. a boxplot), this will not work with any version of matplotlib newer than
1.1.0. If you’re working from the current github master, this won’t work. I’m not sure what the problem is yet… It may be an unintended change, or it may not be…
Normally, you’d do something along these lines:
import matplotlib.pyplot as plt fig, ax = plt.subplots() # We need to draw the canvas, otherwise the labels won't be positioned and # won't have values yet. fig.canvas.draw() labels = [item.get_text() for item in ax.get_xticklabels()] labels = 'Testing' ax.set_xticklabels(labels) plt.show()
To understand the reason why you need to jump through so many hoops, you need to understand a bit more about how matplotlib is structured.
Matplotlib deliberately avoids doing “static” positioning of ticks, etc, unless it’s explicitly told to. The assumption is that you’ll want to interact with the plot, and so the bounds of the plot, ticks, ticklabels, etc will be dynamically changing.
Therefore, you can’t just set the text of a given tick label. By default, it’s re-set by the axis’s Locator and Formatter every time the plot is drawn.
However, if the Locators and Formatters are set to be static (
FixedFormatter, respectively), then the tick labels stay the same.
This is what
Hopefully that makes it slighly more clear as to why changing an individual tick label is a bit convoluted.
Often, what you actually want to do is just annotate a certain position. In that case, look into
One can also do this with pylab and xticks
import matplotlib import matplotlib.pyplot as plt x = [0,1,2] y = [90,40,65] labels = ['high', 'low', 37337] plt.plot(x,y, 'r') plt.xticks(x, labels, rotation='vertical') plt.show()
In newer versions of
matplotlib, if you do not set the tick labels with a bunch of
str values, they are
'' by default (and when the plot is draw the labels are simply the ticks values). Knowing that, to get your desired output would require something like this:
from pylab import * axes = figure().add_subplot(111) a=axes.get_xticks().tolist() a='change' axes.set_xticklabels(a) [<matplotlib.text.Text object at 0x539aa50>, <matplotlib.text.Text object at 0x53a0c90>, <matplotlib.text.Text object at 0x53a73d0>, <matplotlib.text.Text object at 0x53a7a50>, <matplotlib.text.Text object at 0x53aa110>, <matplotlib.text.Text object at 0x53aa790>] plt.show()
and the result:
and now if you check the
_xticklabels, they are no longer a bunch of
for item in axes.get_xticklabels()] ['0.0', 'change', '1.0', '1.5', '2.0'][item.get_text()
It works in the versions from
1.1.1rc1 to the current version
It’s been a while since this question was asked. As of today (
matplotlib 2.2.2) and after some reading and trials, I think the best/proper way is the following:
Matplotlib has a module named
ticker that “contains classes to support completely configurable tick locating and formatting”. To modify a specific tick from the plot, the following works for me:
import matplotlib.pyplot as plt import matplotlib.ticker as mticker import numpy as np def update_ticks(x, pos): if x == 0: return 'Mean' elif pos == 6: return 'pos is 6' else: return x data = np.random.normal(0, 1, 1000) fig, ax = plt.subplots() ax.hist(data, bins=25, edgecolor='black') ax.xaxis.set_major_formatter(mticker.FuncFormatter(update_ticks)) plt.show()
x is the value of the tick and
pos is its relative position in order in the axis. Notice that
pos takes values starting in
1, not in
0 as usual when indexing.
In my case, I was trying to format the
y-axis of a histogram with percentage values.
mticker has another class named
PercentFormatter that can do this easily without the need to define a separate function as before:
import matplotlib.pyplot as plt import matplotlib.ticker as mticker import numpy as np data = np.random.normal(0, 1, 1000) fig, ax = plt.subplots() weights = np.ones_like(data) / len(data) ax.hist(data, bins=25, weights=weights, edgecolor='black') ax.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1.0, decimals=1)) plt.show()
In this case
xmax is the data value that corresponds to 100%. Percentages are computed as
x / xmax * 100, that’s why we fix
decimals is the number of decimal places to place after the point.
The axes class has a set_yticklabels function which allows you to set the tick labels, like so:
#ax is the axes instance group_labels = ['control', 'cold treatment', 'hot treatment', 'another treatment', 'the last one'] ax.set_xticklabels(group_labels)
I’m still working on why your example above didn’t work.
import matplotlib.pyplot as plt fig, ax1 = plt.subplots(1,1) x1 = [0,1,2,3] squad = ['Fultz','Embiid','Dario','Simmons'] ax1.set_xticks(x1) ax1.set_xticklabels(squad, minor=False, rotation=45)
This also works in matplotlib 3:
x1 = [0,1,2,3] squad = ['Fultz','Embiid','Dario','Simmons'] plt.xticks(x1, squad, rotation=45)
If you do not work with
ax and you want to modify all labels (e.g. for normalization) you can do this:
labels, locations = plt.yticks() plt.yticks(labels, labels/max(labels))