.. _sphx_glr_tutorials_introductory_pyplot.py: =============== Pyplot tutorial =============== An introduction to the pyplot interface. Intro to pyplot =============== :mod:matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In :mod:matplotlib.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes (please note that "axes" here and in most places in the documentation refers to the *axes* part of a figure __ and not the strict mathematical term for more than one axis). .. note:: the pyplot API is generally less-flexible than the object-oriented API. Most of the function calls you see here can also be called as methods from an Axes object. We recommend browsing the tutorials and examples to see how this works. Generating visualizations with pyplot is very quick: .. code-block:: python import matplotlib.pyplot as plt plt.plot([1, 2, 3, 4]) plt.ylabel('some numbers') plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_001.png :align: center You may be wondering why the x-axis ranges from 0-3 and the y-axis from 1-4. If you provide a single list or array to the :func:~matplotlib.pyplot.plot command, matplotlib assumes it is a sequence of y values, and automatically generates the x values for you. Since python ranges start with 0, the default x vector has the same length as y but starts with 0. Hence the x data are [0,1,2,3]. :func:~matplotlib.pyplot.plot is a versatile command, and will take an arbitrary number of arguments. For example, to plot x versus y, you can issue the command: .. code-block:: python plt.plot([1, 2, 3, 4], [1, 4, 9, 16]) .. image:: /tutorials/introductory/images/sphx_glr_pyplot_002.png :align: center Formatting the style of your plot --------------------------------- For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is 'b-', which is a solid blue line. For example, to plot the above with red circles, you would issue .. code-block:: python plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro') plt.axis([0, 6, 0, 20]) plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_003.png :align: center See the :func:~matplotlib.pyplot.plot documentation for a complete list of line styles and format strings. The :func:~matplotlib.pyplot.axis command in the example above takes a list of [xmin, xmax, ymin, ymax] and specifies the viewport of the axes. If matplotlib were limited to working with lists, it would be fairly useless for numeric processing. Generally, you will use numpy _ arrays. In fact, all sequences are converted to numpy arrays internally. The example below illustrates a plotting several lines with different format styles in one command using arrays. .. code-block:: python import numpy as np # evenly sampled time at 200ms intervals t = np.arange(0., 5., 0.2) # red dashes, blue squares and green triangles plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^') plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_004.png :align: center .. _plotting-with-keywords: Plotting with keyword strings ============================= There are some instances where you have data in a format that lets you access particular variables with strings. For example, with :class:numpy.recarray or :class:pandas.DataFrame. Matplotlib allows you provide such an object with the data keyword argument. If provided, then you may generate plots with the strings corresponding to these variables. .. code-block:: python data = {'a': np.arange(50), 'c': np.random.randint(0, 50, 50), 'd': np.random.randn(50)} data['b'] = data['a'] + 10 * np.random.randn(50) data['d'] = np.abs(data['d']) * 100 plt.scatter('a', 'b', c='c', s='d', data=data) plt.xlabel('entry a') plt.ylabel('entry b') plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_005.png :align: center .. _plotting-with-categorical-vars: Plotting with categorical variables =================================== It is also possible to create a plot using categorical variables. Matplotlib allows you to pass categorical variables directly to many plotting functions. For example: .. code-block:: python names = ['group_a', 'group_b', 'group_c'] values = [1, 10, 100] plt.figure(1, figsize=(9, 3)) plt.subplot(131) plt.bar(names, values) plt.subplot(132) plt.scatter(names, values) plt.subplot(133) plt.plot(names, values) plt.suptitle('Categorical Plotting') plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_006.png :align: center .. _controlling-line-properties: Controlling line properties =========================== Lines have many attributes that you can set: linewidth, dash style, antialiased, etc; see :class:matplotlib.lines.Line2D. There are several ways to set line properties * Use keyword args:: plt.plot(x, y, linewidth=2.0) * Use the setter methods of a Line2D instance. plot returns a list of Line2D objects; e.g., line1, line2 = plot(x1, y1, x2, y2). In the code below we will suppose that we have only one line so that the list returned is of length 1. We use tuple unpacking with line, to get the first element of that list:: line, = plt.plot(x, y, '-') line.set_antialiased(False) # turn off antialising * Use the :func:~matplotlib.pyplot.setp command. The example below uses a MATLAB-style command to set multiple properties on a list of lines. setp works transparently with a list of objects or a single object. You can either use python keyword arguments or MATLAB-style string/value pairs:: lines = plt.plot(x1, y1, x2, y2) # use keyword args plt.setp(lines, color='r', linewidth=2.0) # or MATLAB style string value pairs plt.setp(lines, 'color', 'r', 'linewidth', 2.0) Here are the available :class:~matplotlib.lines.Line2D properties. ====================== ================================================== Property Value Type ====================== ================================================== alpha float animated [True | False] antialiased or aa [True | False] clip_box a matplotlib.transform.Bbox instance clip_on [True | False] clip_path a Path instance and a Transform instance, a Patch color or c any matplotlib color contains the hit testing function dash_capstyle ['butt' | 'round' | 'projecting'] dash_joinstyle ['miter' | 'round' | 'bevel'] dashes sequence of on/off ink in points data (np.array xdata, np.array ydata) figure a matplotlib.figure.Figure instance label any string linestyle or ls [ '-' | '--' | '-.' | ':' | 'steps' | ...] linewidth or lw float value in points lod [True | False] marker [ '+' | ',' | '.' | '1' | '2' | '3' | '4' ] markeredgecolor or mec any matplotlib color markeredgewidth or mew float value in points markerfacecolor or mfc any matplotlib color markersize or ms float markevery [ None | integer | (startind, stride) ] picker used in interactive line selection pickradius the line pick selection radius solid_capstyle ['butt' | 'round' | 'projecting'] solid_joinstyle ['miter' | 'round' | 'bevel'] transform a matplotlib.transforms.Transform instance visible [True | False] xdata np.array ydata np.array zorder any number ====================== ================================================== To get a list of settable line properties, call the :func:~matplotlib.pyplot.setp function with a line or lines as argument .. sourcecode:: ipython In [69]: lines = plt.plot([1, 2, 3]) In [70]: plt.setp(lines) alpha: float animated: [True | False] antialiased or aa: [True | False] ...snip .. _multiple-figs-axes: Working with multiple figures and axes ====================================== MATLAB, and :mod:~matplotlib.pyplot, have the concept of the current figure and the current axes. All plotting commands apply to the current axes. The function :func:~matplotlib.pyplot.gca returns the current axes (a :class:matplotlib.axes.Axes instance), and :func:~matplotlib.pyplot.gcf returns the current figure (:class:matplotlib.figure.Figure instance). Normally, you don't have to worry about this, because it is all taken care of behind the scenes. Below is a script to create two subplots. .. code-block:: python def f(t): return np.exp(-t) * np.cos(2*np.pi*t) t1 = np.arange(0.0, 5.0, 0.1) t2 = np.arange(0.0, 5.0, 0.02) plt.figure(1) plt.subplot(211) plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k') plt.subplot(212) plt.plot(t2, np.cos(2*np.pi*t2), 'r--') plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_007.png :align: center The :func:~matplotlib.pyplot.figure command here is optional because figure(1) will be created by default, just as a subplot(111) will be created by default if you don't manually specify any axes. The :func:~matplotlib.pyplot.subplot command specifies numrows, numcols, plot_number where plot_number ranges from 1 to numrows*numcols. The commas in the subplot command are optional if numrows*numcols<10. So subplot(211) is identical to subplot(2, 1, 1). You can create an arbitrary number of subplots and axes. If you want to place an axes manually, i.e., not on a rectangular grid, use the :func:~matplotlib.pyplot.axes command, which allows you to specify the location as axes([left, bottom, width, height]) where all values are in fractional (0 to 1) coordinates. See :ref:sphx_glr_gallery_subplots_axes_and_figures_axes_demo.py for an example of placing axes manually and :ref:sphx_glr_gallery_subplots_axes_and_figures_subplot_demo.py for an example with lots of subplots. You can create multiple figures by using multiple :func:~matplotlib.pyplot.figure calls with an increasing figure number. Of course, each figure can contain as many axes and subplots as your heart desires:: import matplotlib.pyplot as plt plt.figure(1) # the first figure plt.subplot(211) # the first subplot in the first figure plt.plot([1, 2, 3]) plt.subplot(212) # the second subplot in the first figure plt.plot([4, 5, 6]) plt.figure(2) # a second figure plt.plot([4, 5, 6]) # creates a subplot(111) by default plt.figure(1) # figure 1 current; subplot(212) still current plt.subplot(211) # make subplot(211) in figure1 current plt.title('Easy as 1, 2, 3') # subplot 211 title You can clear the current figure with :func:~matplotlib.pyplot.clf and the current axes with :func:~matplotlib.pyplot.cla. If you find it annoying that states (specifically the current image, figure and axes) are being maintained for you behind the scenes, don't despair: this is just a thin stateful wrapper around an object oriented API, which you can use instead (see :ref:sphx_glr_tutorials_intermediate_artists.py) If you are making lots of figures, you need to be aware of one more thing: the memory required for a figure is not completely released until the figure is explicitly closed with :func:~matplotlib.pyplot.close. Deleting all references to the figure, and/or using the window manager to kill the window in which the figure appears on the screen, is not enough, because pyplot maintains internal references until :func:~matplotlib.pyplot.close is called. .. _working-with-text: Working with text ================= The :func:~matplotlib.pyplot.text command can be used to add text in an arbitrary location, and the :func:~matplotlib.pyplot.xlabel, :func:~matplotlib.pyplot.ylabel and :func:~matplotlib.pyplot.title are used to add text in the indicated locations (see :ref:sphx_glr_tutorials_text_text_intro.py for a more detailed example) .. code-block:: python mu, sigma = 100, 15 x = mu + sigma * np.random.randn(10000) # the histogram of the data n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75) plt.xlabel('Smarts') plt.ylabel('Probability') plt.title('Histogram of IQ') plt.text(60, .025, r'$\mu=100,\ \sigma=15$') plt.axis([40, 160, 0, 0.03]) plt.grid(True) plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_008.png :align: center All of the :func:~matplotlib.pyplot.text commands return an :class:matplotlib.text.Text instance. Just as with with lines above, you can customize the properties by passing keyword arguments into the text functions or using :func:~matplotlib.pyplot.setp:: t = plt.xlabel('my data', fontsize=14, color='red') These properties are covered in more detail in :ref:sphx_glr_tutorials_text_text_props.py. Using mathematical expressions in text -------------------------------------- matplotlib accepts TeX equation expressions in any text expression. For example to write the expression :math:\sigma_i=15 in the title, you can write a TeX expression surrounded by dollar signs:: plt.title(r'$\sigma_i=15$') The r preceding the title string is important -- it signifies that the string is a *raw* string and not to treat backslashes as python escapes. matplotlib has a built-in TeX expression parser and layout engine, and ships its own math fonts -- for details see :ref:sphx_glr_tutorials_text_mathtext.py. Thus you can use mathematical text across platforms without requiring a TeX installation. For those who have LaTeX and dvipng installed, you can also use LaTeX to format your text and incorporate the output directly into your display figures or saved postscript -- see :ref:sphx_glr_tutorials_text_usetex.py. Annotating text --------------- The uses of the basic :func:~matplotlib.pyplot.text command above place text at an arbitrary position on the Axes. A common use for text is to annotate some feature of the plot, and the :func:~matplotlib.pyplot.annotate method provides helper functionality to make annotations easy. In an annotation, there are two points to consider: the location being annotated represented by the argument xy and the location of the text xytext. Both of these arguments are (x,y) tuples. .. code-block:: python ax = plt.subplot(111) t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) line, = plt.plot(t, s, lw=2) plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.05), ) plt.ylim(-2, 2) plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_009.png :align: center In this basic example, both the xy (arrow tip) and xytext locations (text location) are in data coordinates. There are a variety of other coordinate systems one can choose -- see :ref:annotations-tutorial and :ref:plotting-guide-annotation for details. More examples can be found in :ref:sphx_glr_gallery_text_labels_and_annotations_annotation_demo.py. Logarithmic and other nonlinear axes ==================================== :mod:matplotlib.pyplot supports not only linear axis scales, but also logarithmic and logit scales. This is commonly used if data spans many orders of magnitude. Changing the scale of an axis is easy: plt.xscale('log') An example of four plots with the same data and different scales for the y axis is shown below. .. code-block:: python from matplotlib.ticker import NullFormatter # useful for logit scale # Fixing random state for reproducibility np.random.seed(19680801) # make up some data in the interval ]0, 1[ y = np.random.normal(loc=0.5, scale=0.4, size=1000) y = y[(y > 0) & (y < 1)] y.sort() x = np.arange(len(y)) # plot with various axes scales plt.figure(1) # linear plt.subplot(221) plt.plot(x, y) plt.yscale('linear') plt.title('linear') plt.grid(True) # log plt.subplot(222) plt.plot(x, y) plt.yscale('log') plt.title('log') plt.grid(True) # symmetric log plt.subplot(223) plt.plot(x, y - y.mean()) plt.yscale('symlog', linthreshy=0.01) plt.title('symlog') plt.grid(True) # logit plt.subplot(224) plt.plot(x, y) plt.yscale('logit') plt.title('logit') plt.grid(True) # Format the minor tick labels of the y-axis into empty strings with # NullFormatter, to avoid cumbering the axis with too many labels. plt.gca().yaxis.set_minor_formatter(NullFormatter()) # Adjust the subplot layout, because the logit one may take more space # than usual, due to y-tick labels like "1 - 10^{-3}" plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25, wspace=0.35) plt.show() .. image:: /tutorials/introductory/images/sphx_glr_pyplot_010.png :align: center It is also possible to add your own scale, see :ref:adding-new-scales for details. **Total running time of the script:** ( 0 minutes 0.479 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:Download Python source code: pyplot.py  .. container:: sphx-glr-download :download:Download Jupyter notebook: pyplot.ipynb  .. only:: html .. rst-class:: sphx-glr-signature Gallery generated by Sphinx-Gallery _