By using the __call__ method it is possible to define classes in a way that the instances will be callable objects. Let's get started by importing matplotlib.pyplot and using %matplotlib Jupyter magic to display plots in the notebook.. import numpy as np import matplotlib.pyplot as plt %matplotlib inline Basic Plotting A callable object is an object which can be used and behaves like a function but might not be a function. %lsmagic =It lists all the available magic function for the Jupyter lab. There are two kinds of magics, line-oriented and cell-oriented. The %matplotlib inline magic command allows you to visualize graph inside jupyter notebook. Jupyter qtconsole. Always call the magic function before importing the matplotlib library. Matplotlib comes with a set of default settings that allow customizing all kinds of properties. Matplotlib is a Python package for 2D plotting and the matplotlib.pyplot sub-module contains many plotting functions to create various kinds of plots. Some of the magic methods in Python directly map to built-in functions; in this case, how to invoke them is fairly obvious. Functions are callable objects. This magic commands allows you to do 3 things — Lists all environment variables, get the value of a particular environment variable and set … You can otherwise end the interaction using the end interaction button and then make a new plot. 2. Use the magic … Matplotlib is one of the most popular Python packages used for data visualization. However, in a Jupyter notebook, we can simply output the graphs within the notebook itself by running the %matplotlib inline magic command. Jupyter console. Set Environment variables. ... %matplotlib. This magic is an absolute must-have! To connect to a GUI loop, execute the %matplotlib magic at your IPython prompt. The __call__ method is called, if the instance is called "like a function", i.e. Jupyter Notebook. It pollutes namespaces with functions that will shadow Python built-ins and can lead to hard-to-track bugs. Matplotlib. However, in other cases, the invocation is far less obvious. Its basic structure is %matplotlib [-l] [gui] and this magics sets up matplotlib. For example, Matplotlib now directly advises against this in its own tutorials: “[pylab] still exists for historical reasons, but it is highly advised not to use. It allows the output of plotting command to be displayed inline i.e. There's more detail on exactly what this does at IPython's documentation on GUI event loops . If you're using Jupyter Notebook, the same commands are available, but people commonly use a specific argument to the %matplotlib magic: %matplotlib is a magic function in IPython. By doing this you don’t need to call the magic function again for a new plot. The magic function system provides a series of functions which allow you to control the behavior of IPython itself, plus a lot of system-type features. using brackets. When running python using the command line, the graphs are typically shown in a separate window. 1. %matplotlib inline import matplotlib.pyplot as plt import numpy as np x = np.arange(20) y = x**2 plt.plot(x, y) %qtconsole: Jupyter notebook support three ways to connect to the kernel. This appendix is devoted to exposing non-obvious syntax that leads to magic methods getting called. You can control the defaults of almost every property in matplotlib: figure size and dpi, line width, color and style, axes, axis and grid properties, text and font properties and so on. Matplotlib can output graphs using various backend graphics libraries, such as Tk, wxPython, etc. I’ll quote the relevant documentation here for you to read for convenience: IPython has a set of predefined ‘magic functions’ that you can call with a command line style syntax. %matplotlib inline = Most people must be already knowing about this. 3. in Jupyter lab UI. Run the magic function before every plot you make otherwise it will overwrite the previous plot. It can be useful if you want to explore all the available magic functions.