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This article will explain in detail how to use Matplotlib in Python. The editor thinks it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.

0. Preface

For the convenience of the following examples, let's first import a few libraries we need. The following code runs in Jupyter Notebook.

% matplotlib inline import matplotlib.pyplot as pltimport numpy as npimport pandas as pd1. Two basic methods of creating Figure 1.1 the first method

Note that Figure is a proper noun in Matplotlib (explained later), and Matplotlib plots your data in Figure (e.g., windows, Jupyter wgets, etc.) Medium. One of the basic ways to create a Figure is to use pyplot.subplots.

As shown below, we create a Figure with pyplot.subplots that contains an axes (again, this is a Matplotlib proper noun, which will be explained later), and then draw using axes.plot.

Fig, ax = plt.subplots () # Create a figure containing a single axes.x = np.arange Fs = 100 # 100Hz sampling rateFsin = 2 # 2Hz y = np.sin (2roomnp.pirated Fsin * (1/Fs) * x) ax.plot (x, y) # Plot some data on the axes.

The above code draws a sine wave waveform.

1.2 the second method

Many other drawing libraries or languages do not require you to explicitly (explicity) create a Figure and axes first. For example, in Matlab, you directly call the plot () function as shown below to get the desired picture in one step. If there is an axes, matlab will create one for you.

Plot ([1,2,3,4], [1,4,2,3])% MATLAB plot.

In fact, you can also use Matplotlib in this way. For each axes drawing method, there is a corresponding function in the matplotlib.pyplot module that performs the function of drawing a picture on the current axes. If you have not already created an axes, create a Figure and axes for you first, as in Matlab. So the above example can be written more succinctly as follows:

Plt.plot (x, y) # Call plt.plot () directly

You will also get the same picture as above.

2. Anatomical diagram and various basic concepts of Figure

The image above is from Ref1. The picture shows the Figure in matplotlib (not sure how to translate this. I thought it could be translated into canvas, but the canvas corresponds to Canvas in English. Figure's description clearly points out that Figure contains Canvas, and if Canvas refers to the canvas, then Figure actually refers to the whole painting. Let's not force it, just use English words)

2.1 Figure

The whole figure. The whole picture or the whole picture. Figure keeps all the child axes information in it, as well as some special artists (titles, figure legends, etc) (here artist is also a word that is difficult to translate, referring to the illustrative information objects of titles, legends, etc.), and canvases (canvas).

It can be said that Figure is the big Boss behind the scenes that manages all the information, and it really performs the action of drawing pictures for you. But as a user, it is less conspicuous and more or less invisible to you. A Figure can contain any number of axes, usually at least one.

Here are several ways to create a Figure, the first two of which have been introduced and are commonly used in the above examples.

The last one is to create an empty Figure, which you can add axes to later. This practice is uncommon and belongs to high-level usage, which makes it convenient for high-level users to have a more flexible axes layout.

Fig, ax = plt.subplots () # a figure with a single Axesfig, axs = plt.subplots (2,2) # a figure with a 2x2 grid of Axesfig = plt.figure () # an empty figure with no Axes2.2 Axes

Axes is originally a plural of axis (coordinates), so it can be translated into a coordinate system. Let's just understand it. A given Figure can contain multiple axes, but an axes object can only exist in one Figure. An Axes contains multiple axis. For example, there are two axes in a 2D (2D) graph and three axes in a 3D (3D) graph.

You can set some properties of a diagram in an axes in the following ways:

Set_xlim (), set_ylim (): used to set the representation range of x-axis and y-axis respectively

Set_title (): sets the title of the diagram

Set_xlabel (), set_ylabel (): labels for x-axis and y-axis, respectively

The Axes class and its member functions are the main entrances to matplolib's object-oriented interface (see instructions below for object-oriented interfaces and pyplot interfaces)

2.3 Axis

This is what we often call axes. I won't repeat it. In fact, the more you explain, the more confused you get. Interested friends can take a look at the original description of the matplotlib document, anyway, the more I read it, the more dizzy I get. You might as well just look at the code example until you know how to use it.

2.4 Artist

Basically, everything you can see on the figure is an artist (even the Figure, Axes, and Axis objects). This includes Text objects, Line2D objects, collections objects, Patch objects... (you get the ea). When the figure is rendered, all of the artists are drawn to the canvas. Most Artists are tied to an Axes; such an Artist cannot be shared by multiple Axes, or moved from one to another.

Dizzy food. As above, you might as well just look at the code example until you know how to use it.

3. Input of drawing function

All drawing functions accept numpy.array or numpy.ma.masked_array as input.

Objects such as data objects in pandas and 'array-like'' objects such as numpy.matrix can produce unexpected results if they are directly used as input to the drawing function. So it's best to convert them to numpy.array objects and then pass them to the drawing function.

# For example, to convert a pandas.DataFramea = pd.DataFrame (np.random.rand (4,5), columns = list ('abcde')) a_asarray = a.values # and to convert a numpy.matrixb = np.matrix ([[1,2], [3,4]]) b_asarray = np.asarray (b) 4. Object-oriented interface and pyplot interface

As mentioned above, there are two basic ways to use Matplotlib.

(1) explicitly create Figure and axes, and then call methods to act on them, which is called an object-oriented style.

(2) call pyplot directly for drawing, which is called shortcut style.

Examples of how to use an object-oriented style:

X = np.linspace (0,2,100) # Note that even in the OO-style, we use `. Pyplot.figure` to create the figure.fig, ax = plt.subplots () # Create a figure and an axes.ax.plot (x, x, label='linear') # Plot some data on the axes.ax.plot (x, x, Create a figure and an axes.ax.plot, label='quadratic') # Plot more data on the axes...ax.plot (x, x calendar 3, label='cubic') #... And some more.ax.set_xlabel ('xlabel') # Add an x-label to the axes.ax.set_ylabel ('ylabel') # Add a y-label to the axes.ax.set_title ("Simple Plot") # Add a title to the axes.ax.legend () # Add a legend.

Example of how to use the pyplot style:

X = np.linspace (0,2,100) plt.plot (x, x, label='linear') # Plot some data on the (implicit) axes.plt.plot (x, x / x / 2, label='quadratic') # etc.plt.plot (x, x / 2, label='cubic') plt.xlabel ('xlabel') plt.ylabel ('ylabel') plt.title ("Simple Plot") plt.legend ()

The above code also gives the methods or function use examples of label, title, legend and other settings in the two styles.

The above two code examples produce the same drawing effect:

In addition, there is a third method for embedded Matplotlib in GUI applications. This is an advanced usage and will not be introduced in this article.

The object-oriented style is as functional as the pyplot style, and you can choose to use either style. But it is best to choose one of them to use, not obscure. In general, it is recommended that you use the pyplot style only in interactive drawings (such as Jupyter notebook), while object-oriented styles are recommended in non-interactive drawings.

Note that in some older code examples, you can also see the use of the pylab interface. But this is no longer recommended (This approach is strongly discouraged nowadays and deprecated). It's only mentioned here because you might see it once in a while, but don't use it in the new code.

5. An example of practical function for drawing reuse

Usually we will find that we need to draw a lot of similar graphs over and over again, just using different data. In order to improve efficiency and reduce errors, consider encapsulating some drawing processing into a function to facilitate reuse and avoid too much redundant code. Here is an example of such a template:

Def my_plotter (ax, data1, data2 Param_dict): "A helper function to make a graph Parameters-ax: Axes The axes to draw to data1: array The x data data2: array The y data param_dict: dict Dictionary of kwargs to pass to ax.plot Returns-out: list list of artists added"out = ax.plot (data1, data2) * * param_dict) return out

Then you can use it in the following ways:

X = np.linspace (0,5,20) fig, ax = plt.subplots () x2 = x**2x3 = x**3my_plotter (ax, x, x2, {'marker': 'o'}) my_plotter (ax, x, x3, {' marker': 'd'})

Or, if you need more than one subgraph

X = np.linspace (0,5,20) x2 = x**2x3 = x**3fig, (ax1, ax2) = plt.subplots (1,2) my_plotter (ax1, x, x2, {'marker': 'x'}) my_plotter (ax2, x, x3, {' marker': 'o'}) fig, ax = plt.subplots (1,2) my_plotter (ax [0], x, x2, {'marker': 'x'}) my_plotter (ax [1], x X3, {'marker':' o'})

Note that, as shown in the above code example, when multiple subgraphs are created, there are two ways to reference axes. In the first way, you directly assign two axes (one axes for each subgraph) to ax1 and ax2 at creation time. In the first way, you assign two axes directly to an axes array ax at creation time, and then reference them in the format of ax [0] and ax [1].

Ref1: Usage Gue-Matplotlib 3.4.3 documentation

This is the end of the article on "how to use Matplotlib in Python". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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