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Matplotlib is a plotting library for Python. It can be used in conjunction with NumPy, providing an efficient open-source alternative to MatLab. It can also be used with graphical toolkits like PyQt and wxPython. Installation via pip3: pip3 install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple On Linux systems, you can also use the Linux package manager to install: * Debian / Ubuntu: sudo apt-get install python-matplotlib * Fedora / Redhat: sudo yum install python-matplotlib After installation, you can use the `python -m pip list` command to check if the matplotlib module is installed. $ pip3 list | grep matplotlib matplotlib 3.3.0 ### Example ## Example import numpy as np from matplotlib import pyplot as plt x = np.arange(1,11)y = 2 * x + 5 plt.title("Matplotlib demo")plt.xlabel("x axis caption")plt.ylabel("y axis caption")plt.plot(x,y)plt.show() In the above example, the `np.arange()` function creates the values for the x-axis. The corresponding values for the y-axis are stored in another array object `y`. These values are plotted using the `plot()` function from the `pyplot` submodule of the matplotlib package. The graph is displayed by the `show()` function. !(#) ### Displaying Chinese in Graphs Matplotlib does not support Chinese characters by default. We can use the following simple method to resolve this. Here we use Source Han Sans, an open-source font released by Adobe and Google. Official website: [https://source.typekit.com/source-han-serif/cn/](https://source.typekit.com/source-han-serif/cn/) GitHub address: [https://github.com/adobe-fonts/source-han-sans/tree/release/OTF/SimplifiedChinese](https://github.com/adobe-fonts/source-han-sans/tree/release/OTF/SimplifiedChinese) After opening the link, select one from there: !(#) You can also download it from the cloud drive: [https://pan.baidu.com/s/10-w1JbXZSnx3Tm6uGpPGOw](https://pan.baidu.com/s/10-w1JbXZSnx3Tm6uGpPGOw), extraction code: yxqu. You can download an OTF font, for example, `SourceHanSansSC-Bold.otf`, and place this file in the directory where your code is currently executing: Place the `SourceHanSansSC-Bold.otf` file in the directory where your code is currently executing: ## Example import numpy as np from matplotlib import pyplot as plt import matplotlib zhfont1 = matplotlib.font_manager.FontProperties(fname="SourceHanSansSC-Bold.otf")x = np.arange(1,11)y = 2 * x + 5 plt.title(" - Test", fontproperties=zhfont1)plt.xlabel("x axis", fontproperties=zhfont1)plt.ylabel("y axis", fontproperties=zhfont1)plt.plot(x,y)plt.show() The execution output is as shown in the following figure: !(#) > Additionally, we can use the system's fonts: > > from matplotlib import pyplot as plt import matplotlib a=sorted([f.name for f in matplotlib.font_manager.fontManager.ttflist])for i in a: print(i) > This prints all the registered names in your `font_manager`'s `ttflist`. Find a Chinese font you like, for example: STFangsong (FangSong), then add the following code: > > plt.rcParams['font.family']=['STFangsong'] As an alternative to a line plot, you can display discrete values by adding a format string to the `plot()` function. The following formatting characters can be used. | Character | Description | | --- | --- | | `'-'` | Solid line style | | `'--'` | Dashed line style | | `'-.'` | Dash-dot line style | | `':'` | Dotted line style | | `'.'` | Point marker | | `','` | Pixel marker | | `'o'` | Circle marker | | `'v'` | Triangle down marker | | `'^'` | Triangle up marker | | `'<'` | Triangle left marker | | `'>'` | Triangle right marker | | `'1'` | Tri_down marker | | `'2'` | Tri_up marker | | `'3'` | Tri_left marker | | `'4'` | Tri_right marker | | `'s'` | Square marker | | `'p'` | Pentagon marker | | `'*'` | Star marker | | `'h'` | Hexagon1 marker | | `'H'` | Hexagon2 marker | | `'+'` | Plus marker | | `'x'` | X marker | | `'D'` | Diamond marker | | `'d'` | Thin_diamond marker | | `'|'` | Vline marker | | `'_'` | Hline marker | Here are the abbreviations for colors: | Character | Color | | --- | --- | | `'b'` | Blue | | `'g'` | Green | | `'r'` | Red | | `'c'` | Cyan | | `'m'` | Magenta | | `'y'` | Yellow | | `'k'` | Black | | `'w'` | White | To display circles representing points instead of the lines in the example above, use `'ob'` as the format string in the `plot()` function. ## Example import numpy as np from matplotlib import pyplot as plt x = np.arange(1,11)y = 2 * x + 5 plt.title("Matplotlib demo")plt.xlabel("x axis caption")plt.ylabel("y axis caption")plt.plot(x,y,"ob")plt.show() The execution output is as shown in the following figure: !(#) ### Plotting a Sine Wave The following example uses matplotlib to generate a sine wave plot. ## Example import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 3 * np.pi, 0.1)y = np.sin(x)plt.title("sine wave form")plt.plot(x, y)plt.show() The execution output is as shown in the following figure: !(#) ### subplot() The `subplot()` function allows you to plot different things in the same figure. The following example plots sine and cosine values: ## Example import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 3 * np.pi, 0.1)y_sin = np.sin(x)y_cos = np.cos(x)plt.subplot(2, 1, 1)plt.plot(x, y_sin)plt.title('Sine')plt.subplot(2, 1, 2)plt.plot(x, y_cos)plt.title('Cosine')plt.show() The execution output is as shown in the following figure: !(#) ### bar() The `pyplot` submodule provides the `bar()` function to generate bar charts. The following example generates a bar chart for two sets of x and y arrays. ## Example from matplotlib import pyplot as plt x = [5,8,10]y = [12,16,6]x2 = [6,9,11]y2 = [6,15,7]plt.bar(x, y, align = 'center')plt.bar(x2, y2, color = 'g', align = 'center')plt.title('Bar graph')plt.ylabel('Y axis')plt.xlabel('X axis')plt.show() The execution output is as shown in the following figure: !(#) ### numpy.histogram() The `numpy.histogram()` function is a graphical representation of the frequency distribution of data. Rectangles of equal horizontal size correspond to class intervals, called bins, and the variable `height` corresponds to frequency. The `numpy.histogram()` function takes the input array and bins as two arguments. The consecutive elements in the bin array are used as boundaries for each bin. ## Example import numpy as np a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])np.histogram(a,bins = [0,20,40,60,80,100])hist,bins = np.histogram(a,bins = [0,20,40,60,80,100])print(hist)print(bins) The output is: ### plt() Matplotlib can convert the numerical representation of a histogram into a graph. The `plt()` function from the `pyplot` submodule takes an array containing the data and the bin array as arguments and converts it into a histogram. ## Example from matplotlib import pyplot as plt import numpy as np a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])plt.hist(a, bins = [0,20,40,60,80,100])plt.title("histogram")plt.show() The execution output is as shown in the following figure: !(#) > More references for Matplotlib: > > > > > * (http://matplotlib.sourceforge.net/users/index.html) > * (http://matplotlib.sourceforge.net/faq/index.html) > * (http://matplotlib.sourceforge.net/users/screenshots.html)