Data Analytics using Python Visualizations - Working with Layouts - Row / Column / Grid

This video tutorial explores different layout options in Bouquet, including rows, columns, and custom grids. Through practical examples, it demonstrates how to create and arrange plots using sales data from three cities over several years. The tutorial covers creating row and column layouts, as well as a custom grid layout, providing foundational knowledge for working with Bouquet layouts.

What are the three types of layouts provided by Bokeh?

• Pages, Sections, and Paragraphs

• Tables, Charts, and Graphs

• Lines, Circles, and Squares

• Rows, Columns, and Grids

In a row-wise layout, how are the plots arranged?

• In a circular pattern

• Vertically, one below the other

• Horizontally, side by side

• In a diagonal line

What is the primary difference between row-wise and column-wise layouts in Bokeh?

• Row-wise layouts are for text, column-wise are for images

• Row-wise layouts use circles, column-wise use squares

• Row-wise layouts are horizontal, column-wise are vertical

• Row-wise layouts are vertical, column-wise are horizontal

How many rows and columns are used in the custom grid layout example?

• One row and four columns

• Four rows and one column

• Three rows and two columns

• Two rows and three columns

Which function is used to create a custom grid layout in Bokeh?

• layout()

• row()

• gridplot()

• column()

In the custom grid layout, where is the second figure placed?

• Position #1 on row two

• Position #3 on row one

• Position #2 on row two

• Position #1 on row one

What is the purpose of the 'show' function in Bokeh?

• To delete the plot

• To export the plot as an image

• To display the plots on the screen

• To save the plot to a file