To sum upAdding %matplotlib inline plots affix the visualization with the code. It prevents the overhead of re-executing the code block every time. In this article, We will see
%matplotlib inline Uses ( Scenario Analysis):
Suppose if we are working on some complex data visualization. And we need to share the code base and Analysis with some others. Usually, we can share the complete notebook. But It will be a passive code only. Especially, For the visualization part, We need to execute the code block.
Further In the above scenario, If we use %matplotlib inline, It will affix the visualization, Hence If we share the code notebook, The visualization will be there without executing it.
Matplotlib inline Implementation-
Adding matplotlib inline plots is a single line process. We only add the below line in the existing code block.
Just for more clarity, Let’s add the above statement in the code block.
%matplotlib inline import matplotlib.pyplot as plt plt.vlines(7, 0, 8, linestyles ="solid", colors ="red") plt.xlim(0, 10) plt.ylim(0, 10) plt.show()
We do not need to run the code each time for visualization of the block after sharing.
Difference Between Matplotlib inline and Matplotlib qt –
We are aware of Matplotlib inline. Moreover, Matplotlib qt creates an external frame for data visualization. It actually creates a separate box for the visual part of the code output. Also as it is a separate frame, Hence it is not inline.
As a data scientist, Usually, We need to give presentations to stakeholders. This presentation may involve data and visual insights. Here, We might not get enough time to re-execute each code block. Matplotlib inline feature helps us to overcome these issues.
Matplotlib inline Actually preserves the visualization section inline with the code part. This is also one of the features of Notebook like Jupyter , Colab , etc which makes executable code sharing more smooth.
To sum up, I hope this article must have cleared the concepts around the uses of Matplotlib inline in python with implementation. Please comment on your views and suggestions.
Data Science Learner Team
Join our list
Subscribe to our mailing list and get interesting stuff and updates to your email inbox.