Data Science and Machine Learning (Theory and Projects) A to Z - Matplotlib for Data Visualization: Matplotlib Set Funct

The video tutorial explains the differences between using PLT and AX for graph annotations in Matplotlib. It covers how to set X and Y labels and limits using both methods, emphasizing the convenience of the set function for configuring multiple properties at once. A practical example is provided to illustrate these concepts. The video concludes with a brief introduction to the next topic, which will focus on different types of markers available in Matplotlib.

What is the primary difference between using PLT and AX for setting graph annotations?

• PLT is used for setting multiple properties at once.

• PLT is a part of Matplotlib's pyplot module, while AX is an object for axes.

• AX is used for creating 3D plots.

• AX allows setting properties individually.

How can you set multiple properties at once using the AX object?

• By using a loop to iterate over properties.

• By calling each property individually.

• By using a separate configuration file.

• By using the set function.

What is the advantage of using the set function with AX?

• It is the only way to set axis limits.

• It allows setting properties in a single function call.

• It automatically adjusts the plot size.

• It provides a graphical user interface for setting properties.

In the practical example, what X limit is set using the set function?

• 1 to 10

• -1 to 11

• -5 to 5

• 0 to 10

What title is given to the plot in the example using the set function?

• Sine and Cosine Waves

• Mathematical Plot

• Graph of Functions

• Sine, Cos, curves

What flexibility does Matplotlib offer according to the video?

• It provides multiple ways to achieve the same result.

• It allows for 3D plotting only.

• It restricts users to a single method for setting properties.

• It only supports line plots.

What topic is hinted at for the next video?

• Advanced graph annotations

• Different types of markers

• 3D plotting techniques

• Data visualization with pandas