.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorials_text_text_intro.py: ======================== Text in Matplotlib Plots ======================== Introduction to plotting and working with text in Matplotlib. Matplotlib has extensive text support, including support for mathematical expressions, truetype support for raster and vector outputs, newline separated text with arbitrary rotations, and unicode support. Because it embeds fonts directly in output documents, e.g., for postscript or PDF, what you see on the screen is what you get in the hardcopy. `FreeType `_ support produces very nice, antialiased fonts, that look good even at small raster sizes. Matplotlib includes its own :mod:`matplotlib.font_manager` (thanks to Paul Barrett), which implements a cross platform, `W3C `_ compliant font finding algorithm. The user has a great deal of control over text properties (font size, font weight, text location and color, etc.) with sensible defaults set in the :doc:`rc file `. And significantly, for those interested in mathematical or scientific figures, Matplotlib implements a large number of TeX math symbols and commands, supporting :doc:`mathematical expressions ` anywhere in your figure. Basic text commands =================== The following commands are used to create text in the pyplot interface and the object-oriented API: =================== =================== ====================================== `.pyplot` API OO API description =================== =================== ====================================== `~.pyplot.text` `~.Axes.text` Add text at an arbitrary location of the `~matplotlib.axes.Axes`. `~.pyplot.annotate` `~.Axes.annotate` Add an annotation, with an optional arrow, at an arbitrary location of the `~matplotlib.axes.Axes`. `~.pyplot.xlabel` `~.Axes.set_xlabel` Add a label to the `~matplotlib.axes.Axes`\'s x-axis. `~.pyplot.ylabel` `~.Axes.set_ylabel` Add a label to the `~matplotlib.axes.Axes`\'s y-axis. `~.pyplot.title` `~.Axes.set_title` Add a title to the `~matplotlib.axes.Axes`. `~.pyplot.figtext` `~.Figure.text` Add text at an arbitrary location of the `.Figure`. `~.pyplot.suptitle` `~.Figure.suptitle` Add a title to the `.Figure`. =================== =================== ====================================== All of these functions create and return a `.Text` instance, which can be configured with a variety of font and other properties. The example below shows all of these commands in action, and more detail is provided in the sections that follow. .. code-block:: default import matplotlib import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) fig.subplots_adjust(top=0.85) # Set titles for the figure and the subplot respectively fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold') ax.set_title('axes title') ax.set_xlabel('xlabel') ax.set_ylabel('ylabel') # Set both x- and y-axis limits to [0, 10] instead of default [0, 1] ax.axis([0, 10, 0, 10]) ax.text(3, 8, 'boxed italics text in data coords', style='italic', bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10}) ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15) ax.text(3, 2, 'unicode: Institut für Festkörperphysik') ax.text(0.95, 0.01, 'colored text in axes coords', verticalalignment='bottom', horizontalalignment='right', transform=ax.transAxes, color='green', fontsize=15) ax.plot([2], [1], 'o') ax.annotate('annotate', xy=(2, 1), xytext=(3, 4), arrowprops=dict(facecolor='black', shrink=0.05)) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_001.png :alt: bold figure suptitle, axes title :class: sphx-glr-single-img Labels for x- and y-axis ======================== Specifying the labels for the x- and y-axis is straightforward, via the `~matplotlib.axes.Axes.set_xlabel` and `~matplotlib.axes.Axes.set_ylabel` methods. .. code-block:: default import matplotlib.pyplot as plt import numpy as np x1 = np.linspace(0.0, 5.0, 100) y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) fig, ax = plt.subplots(figsize=(5, 3)) fig.subplots_adjust(bottom=0.15, left=0.2) ax.plot(x1, y1) ax.set_xlabel('time [s]') ax.set_ylabel('Damped oscillation [V]') plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_002.png :alt: text intro :class: sphx-glr-single-img The x- and y-labels are automatically placed so that they clear the x- and y-ticklabels. Compare the plot below with that above, and note the y-label is to the left of the one above. .. code-block:: default fig, ax = plt.subplots(figsize=(5, 3)) fig.subplots_adjust(bottom=0.15, left=0.2) ax.plot(x1, y1*10000) ax.set_xlabel('time [s]') ax.set_ylabel('Damped oscillation [V]') plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_003.png :alt: text intro :class: sphx-glr-single-img If you want to move the labels, you can specify the *labelpad* keyword argument, where the value is points (1/72", the same unit used to specify fontsizes). .. code-block:: default fig, ax = plt.subplots(figsize=(5, 3)) fig.subplots_adjust(bottom=0.15, left=0.2) ax.plot(x1, y1*10000) ax.set_xlabel('time [s]') ax.set_ylabel('Damped oscillation [V]', labelpad=18) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_004.png :alt: text intro :class: sphx-glr-single-img Or, the labels accept all the `.Text` keyword arguments, including *position*, via which we can manually specify the label positions. Here we put the xlabel to the far left of the axis. Note, that the y-coordinate of this position has no effect - to adjust the y-position we need to use the *labelpad* kwarg. .. code-block:: default fig, ax = plt.subplots(figsize=(5, 3)) fig.subplots_adjust(bottom=0.15, left=0.2) ax.plot(x1, y1) ax.set_xlabel('time [s]', position=(0., 1e6), horizontalalignment='left') ax.set_ylabel('Damped oscillation [V]') plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_005.png :alt: text intro :class: sphx-glr-single-img All the labelling in this tutorial can be changed by manipulating the `matplotlib.font_manager.FontProperties` method, or by named kwargs to `~matplotlib.axes.Axes.set_xlabel` .. code-block:: default from matplotlib.font_manager import FontProperties font = FontProperties() font.set_family('serif') font.set_name('Times New Roman') font.set_style('italic') fig, ax = plt.subplots(figsize=(5, 3)) fig.subplots_adjust(bottom=0.15, left=0.2) ax.plot(x1, y1) ax.set_xlabel('time [s]', fontsize='large', fontweight='bold') ax.set_ylabel('Damped oscillation [V]', fontproperties=font) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_006.png :alt: text intro :class: sphx-glr-single-img Finally, we can use native TeX rendering in all text objects and have multiple lines: .. code-block:: default fig, ax = plt.subplots(figsize=(5, 3)) fig.subplots_adjust(bottom=0.2, left=0.2) ax.plot(x1, np.cumsum(y1**2)) ax.set_xlabel('time [s] \n This was a long experiment') ax.set_ylabel(r'$\int\ Y^2\ dt\ \ [V^2 s]$') plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_007.png :alt: text intro :class: sphx-glr-single-img Titles ====== Subplot titles are set in much the same way as labels, but there is the *loc* keyword arguments that can change the position and justification from the default value of ``loc=center``. .. code-block:: default fig, axs = plt.subplots(3, 1, figsize=(5, 6), tight_layout=True) locs = ['center', 'left', 'right'] for ax, loc in zip(axs, locs): ax.plot(x1, y1) ax.set_title('Title with loc at '+loc, loc=loc) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_008.png :alt: Title with loc at center, Title with loc at left, Title with loc at right :class: sphx-glr-single-img Vertical spacing for titles is controlled via :rc:`axes.titlepad`, which defaults to 5 points. Setting to a different value moves the title. .. code-block:: default fig, ax = plt.subplots(figsize=(5, 3)) fig.subplots_adjust(top=0.8) ax.plot(x1, y1) ax.set_title('Vertically offset title', pad=30) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_009.png :alt: Vertically offset title :class: sphx-glr-single-img Ticks and ticklabels ==================== Placing ticks and ticklabels is a very tricky aspect of making a figure. Matplotlib does its best to accomplish the task automatically, but it also offers a very flexible framework for determining the choices for tick locations, and how they are labelled. Terminology ~~~~~~~~~~~ *Axes* have an `matplotlib.axis.Axis` object for the ``ax.xaxis`` and ``ax.yaxis`` that contain the information about how the labels in the axis are laid out. The axis API is explained in detail in the documentation to `~matplotlib.axis`. An Axis object has major and minor ticks. The Axis has `.Axis.set_major_locator` and `.Axis.set_minor_locator` methods that use the data being plotted to determine the location of major and minor ticks. There are also `.Axis.set_major_formatter` and `.Axis.set_minor_formatter` methods that format the tick labels. Simple ticks ~~~~~~~~~~~~ It often is convenient to simply define the tick values, and sometimes the tick labels, overriding the default locators and formatters. This is discouraged because it breaks interactive navigation of the plot. It also can reset the axis limits: note that the second plot has the ticks we asked for, including ones that are well outside the automatic view limits. .. code-block:: default fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True) axs[0].plot(x1, y1) axs[1].plot(x1, y1) axs[1].xaxis.set_ticks(np.arange(0., 8.1, 2.)) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_010.png :alt: text intro :class: sphx-glr-single-img We can of course fix this after the fact, but it does highlight a weakness of hard-coding the ticks. This example also changes the format of the ticks: .. code-block:: default fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True) axs[0].plot(x1, y1) axs[1].plot(x1, y1) ticks = np.arange(0., 8.1, 2.) # list comprehension to get all tick labels... tickla = [f'{tick:1.2f}' for tick in ticks] axs[1].xaxis.set_ticks(ticks) axs[1].xaxis.set_ticklabels(tickla) axs[1].set_xlim(axs[0].get_xlim()) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_011.png :alt: text intro :class: sphx-glr-single-img Tick Locators and Formatters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Instead of making a list of all the tickalbels, we could have used `matplotlib.ticker.StrMethodFormatter` (new-style ``str.format()`` format string) or `matplotlib.ticker.FormatStrFormatter` (old-style '%' format string) and passed it to the ``ax.xaxis``. A `matplotlib.ticker.StrMethodFormatter` can also be created by passing a ``str`` without having to explicitly create the formatter. .. code-block:: default fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True) axs[0].plot(x1, y1) axs[1].plot(x1, y1) ticks = np.arange(0., 8.1, 2.) axs[1].xaxis.set_ticks(ticks) axs[1].xaxis.set_major_formatter('{x:1.1f}') axs[1].set_xlim(axs[0].get_xlim()) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_012.png :alt: text intro :class: sphx-glr-single-img And of course we could have used a non-default locator to set the tick locations. Note we still pass in the tick values, but the x-limit fix used above is *not* needed. .. code-block:: default fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True) axs[0].plot(x1, y1) axs[1].plot(x1, y1) locator = matplotlib.ticker.FixedLocator(ticks) axs[1].xaxis.set_major_locator(locator) axs[1].xaxis.set_major_formatter('±{x}°') plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_013.png :alt: text intro :class: sphx-glr-single-img The default formatter is the `matplotlib.ticker.MaxNLocator` called as ``ticker.MaxNLocator(self, nbins='auto', steps=[1, 2, 2.5, 5, 10])`` The *steps* keyword contains a list of multiples that can be used for tick values. i.e. in this case, 2, 4, 6 would be acceptable ticks, as would 20, 40, 60 or 0.2, 0.4, 0.6. However, 3, 6, 9 would not be acceptable because 3 doesn't appear in the list of steps. ``nbins=auto`` uses an algorithm to determine how many ticks will be acceptable based on how long the axis is. The fontsize of the ticklabel is taken into account, but the length of the tick string is not (because its not yet known.) In the bottom row, the ticklabels are quite large, so we set ``nbins=4`` to make the labels fit in the right-hand plot. .. code-block:: default fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True) for n, ax in enumerate(axs.flat): ax.plot(x1*10., y1) formatter = matplotlib.ticker.FormatStrFormatter('%1.1f') locator = matplotlib.ticker.MaxNLocator(nbins='auto', steps=[1, 4, 10]) axs[0, 1].xaxis.set_major_locator(locator) axs[0, 1].xaxis.set_major_formatter(formatter) formatter = matplotlib.ticker.FormatStrFormatter('%1.5f') locator = matplotlib.ticker.AutoLocator() axs[1, 0].xaxis.set_major_formatter(formatter) axs[1, 0].xaxis.set_major_locator(locator) formatter = matplotlib.ticker.FormatStrFormatter('%1.5f') locator = matplotlib.ticker.MaxNLocator(nbins=4) axs[1, 1].xaxis.set_major_formatter(formatter) axs[1, 1].xaxis.set_major_locator(locator) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_014.png :alt: text intro :class: sphx-glr-single-img Finally, we can specify functions for the formatter using `matplotlib.ticker.FuncFormatter`. Further, like `matplotlib.ticker.StrMethodFormatter`, passing a function will automatically create a `matplotlib.ticker.FuncFormatter`. .. code-block:: default def formatoddticks(x, pos): """Format odd tick positions.""" if x % 2: return f'{x:1.2f}' else: return '' fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True) ax.plot(x1, y1) locator = matplotlib.ticker.MaxNLocator(nbins=6) ax.xaxis.set_major_formatter(formatoddticks) ax.xaxis.set_major_locator(locator) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_015.png :alt: text intro :class: sphx-glr-single-img Dateticks ~~~~~~~~~ Matplotlib can accept `datetime.datetime` and `numpy.datetime64` objects as plotting arguments. Dates and times require special formatting, which can often benefit from manual intervention. In order to help, dates have special Locators and Formatters, defined in the `matplotlib.dates` module. A simple example is as follows. Note how we have to rotate the tick labels so that they don't over-run each other. .. code-block:: default import datetime fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True) base = datetime.datetime(2017, 1, 1, 0, 0, 1) time = [base + datetime.timedelta(days=x) for x in range(len(x1))] ax.plot(time, y1) ax.tick_params(axis='x', rotation=70) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_016.png :alt: text intro :class: sphx-glr-single-img We can pass a format to `matplotlib.dates.DateFormatter`. Also note that the 29th and the next month are very close together. We can fix this by using the `.dates.DayLocator` class, which allows us to specify a list of days of the month to use. Similar formatters are listed in the `matplotlib.dates` module. .. code-block:: default import matplotlib.dates as mdates locator = mdates.DayLocator(bymonthday=[1, 15]) formatter = mdates.DateFormatter('%b %d') fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True) ax.xaxis.set_major_locator(locator) ax.xaxis.set_major_formatter(formatter) ax.plot(time, y1) ax.tick_params(axis='x', rotation=70) plt.show() .. image:: /tutorials/text/images/sphx_glr_text_intro_017.png :alt: text intro :class: sphx-glr-single-img Legends and Annotations ======================= - Legends: :doc:`/tutorials/intermediate/legend_guide` - Annotations: :doc:`/tutorials/text/annotations` .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 4.816 seconds) .. _sphx_glr_download_tutorials_text_text_intro.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: text_intro.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: text_intro.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature Keywords: matplotlib code example, codex, python plot, pyplot `Gallery generated by Sphinx-Gallery `_