A Practical Approach to Timeseries Forecasting Using Python - Area Plot

The video tutorial explains how to create and customize an area plot using a data frame. It covers setting the plot type, adjusting figure size, and ensuring columns are not stacked. The tutorial also discusses adding titles and labels, using colors to differentiate data categories like confirmed, cured, and death cases, and customizing these colors for clarity. Finally, it introduces upcoming topics such as autocorrelation and statistical measures.

What is the primary purpose of setting the 'kind' in an area plot?

• To set the axis labels

• To define the data source

• To determine the color scheme

• To specify the type of plot

Why is it important to specify whether an area plot is stacked or not?

• To adjust the plot size

• To determine the plot type

• To decide how data columns are layered

• To set the plot title

In the context of area plots, what does the light green color represent?

• Cured cases

• Death cases

• Confirmed cases

• Recovered cases

What is the significance of using different colors for each data column in an area plot?

• To make the plot more colorful

• To distinguish between different data categories

• To highlight the most important data

• To match the company branding

Which color was chosen for the 'cured' cases in the customized area plot?

• Blue

• Black

• Red

• Green

What is the order of darkness for the colors used in the area plot?

• Black, Green, Blue

• Blue, Green, Black

• Green, Blue, Black

• Black, Blue, Green

Which data column is visually represented at the bottom of the area plot?

• Death cases

• Cured cases

• Recovered cases

• Confirmed cases