Matplotlib is a popular Python library for creating and customizing plots and visualizations. One of the features of Matplotlib is the ability to use it inline, which means that you can display your plots directly in a Jupyter notebook or an IPython console, without having to open a separate window or save them to a file.
I will show you how to use Matplotlib inline and some of the benefits and drawbacks of this mode. I will also give you some tips and tricks to make your plots look better and more interactive.
To use Matplotlib inline, you need to import the library and use the magic command %matplotlib inline at the beginning of your code. This will tell the notebook or console to render the plots as static images and embed them in the output cells. For example:
import matplotlib.pyplot as plt
%matplotlib inline
Now you can create your plots as usual, using the plt object and its methods. For example, let’s plot a simple sine wave:
import numpy as np
x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Sine wave')
plt.show()
The plot is displayed inline in the output cell, below the code cell. You can also zoom in and out of the plot by clicking on it and dragging the mouse.
A key advantage of Matplotlib inline is its convenience for sharing plots, as you can export your notebook or console session directly to PDF or HTML. You can also save your plots as image files by using the plt.savefig() method before calling plt.show(). For example:
plt.savefig('sine_wave.png')
This will save the plot as a PNG file in your current working directory. You can also specify other formats, such as JPG, SVG, or PDF, by changing the file extension.
Matplotlib inline also allows for extensive plot customization through a wide range of parameters and options. For example, you can change the figure size, the font size, the line width, the color scheme, the legend position, and more. You can also add annotations, grids, axes labels, titles, and other elements to your plots. For example, let’s make some changes to our sine wave plot:
plt.figure(figsize=(10, 6)) # increase the figure size
plt.plot(x, y, linewidth=3, color='red') # change the line width and color
plt.xlabel('x', fontsize=14) # increase the font size of x-axis label
plt.ylabel('y', fontsize=14) # increase the font size of y-axis label
plt.title('Sine wave', fontsize=18) # increase the font size of title
plt.grid() # add a grid
plt.legend(['y = sin(x)'], loc='upper right') # add a legend
plt.annotate('maximum', xy=(np.pi/2, 1), xytext=(np.pi/2 + 0.5, 0.8), arrowprops=dict(facecolor='black', shrink=0.05)) # add an annotation with an arrow
plt.show()
The plot looks more appealing and informative with these customizations.
A limitation of Matplotlib inline is the lack of dynamic interaction with plots, such as real-time updates or 3D manipulation. For example, you cannot rotate or pan your plots in 3D mode, or use widgets or sliders to change your plot parameters. You also cannot update your plots in real time based on new data or user input.
To overcome these limitations, you can use other modes of Matplotlib, such as notebook or widget mode. These modes allow you to create interactive plots that can respond to user actions and events. To use these modes, you need to install additional libraries and use different magic commands. For example:
import matplotlib.pyplot as plt
%matplotlib notebook # for notebook mode
%matplotlib widget # for widget mode
These modes will create a toolbar below your plots that will let you manipulate them in various ways.
There are buttons for zooming, panning, rotating, saving, etc. You can also hover over the plot to see the coordinates of each point.
Notebook and widget modes are more suitable for exploratory data analysis and interactive visualization. However, they also have some drawbacks. One of them is that they require more resources and dependencies than inline mode. Another one is that they may not work well with some browsers or platforms. You may also encounter some compatibility issues or bugs when using these modes.
Therefore, you should choose the mode that best suits your needs and preferences. You can also switch between different modes by using different magic commands. However, you should be careful not to mix different modes in the same notebook or console session, as this may cause some errors or unexpected behavior.