A Practical Approach to Timeseries Forecasting Using Python
 - Area Plot

A Practical Approach to Timeseries Forecasting Using Python - Area Plot

Assessment

Interactive Video

Computers

9th - 10th Grade

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

The video tutorial explains how to create and customize an area plot using a data frame. It covers setting plot parameters like kind, stacked option, and figure size. The tutorial also demonstrates adding titles and labels to the plot, and explains the significance of color coding for different data columns such as confirmed, cured, and death cases. It further explores customizing colors for better visualization and concludes with a brief introduction to upcoming topics like autocorrelation and statistical measures.

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7 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What type of plot is being discussed in this section?

Bar plot

Line plot

Scatter plot

Area plot

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of setting the figure size in an area plot?

To determine the color of the plot

To specify the dimensions of the plot

To decide the type of plot

To set the data range

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What label is set for the y-axis in the plot?

Number of COVID cases

Number of positive cases

Confirmed cases

Date

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is the date used on the x-axis?

Because it is the most important data

Because it is set as an index

Because it is easier to read

Because it is the default setting

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What color is used to represent cured cases in the plot?

Black

Green

Blue

Red

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which data column is represented by the darkest color in the plot?

Confirmed cases

Recovered cases

Cured cases

Deaths

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why are the columns 'Confirmed' and 'Cured' placed closer together in the visualization?

Because they have similar values

Because they are less important

Because they are more related to each other

Because they are the same data

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