Mapping COVID-19 Crisis Through Visualization

In this activity, you will create a line chart to analyze the trend of new COVID-19 cases in Denmark over time. The x-axis will represent the Date_reported, and the y-axis will display the number of new cases. The goal is to visualize how the number of cases fluctuated throughout the reported period in the dataset.

• Figure size : 10 by 6

• Title : New COVID-19 Cases in Denmark

• x-axis label: Date

• y-axis label: New Cases

• Line label: "New Cases"

In this activity, you will create a pie chart using the region_counts series to display the distribution of countries across different WHO regions from the dataset. Use the following parameters to format your chart:

• Figure size: 8 x 8

• Title: WHO Regions Distribution

• Percentage display (autopct): %1.1f%%

Finally, apply .tight_layout() and display the plot.

Plot a histogram to visualize the distribution of cumulative deaths using the Cumulative_deaths column. Use the following parameters to format your histogram:

• Figure size : 10 by 6

• Title: Distribution of Cumulative Deaths

• x-axis label: Cumulative Deaths

• y-axis label: Frequency

• Color: Red

• Bins : 10

Apply .tight_layout() and display the plot.

Redo the line plot of new COVID-19 cases in Denmark from the previous section using the series monthly_cases with the following customizations:

• Figure size : 10 by 6

• Line color: Green

• Line style: --

• Line width: 3

• Title: New COVID-19 Cases in Denmark (Use font size 16 and set the font weight to bold)

• x-axis : Date (Use font size 12)

• y-axis labels: New Cases (Use font size 12)

• x-axis rotation: Set the rotation of the x-axis labels to 45 degrees

Apply .tight_layout() and display your plot.

In this task, you will create a pie chart to visualize the distribution of WHO regions, with a special emphasis on the EURO region by exploding its slice by a fraction of 0.1. Use the region_counts series and apply the following customizations:

• Figure size : 8 by 8
• Percentage display (autopct): %1.1f%%
• Starting angle: 90 degrees
• Colors: Defined in the notebook
• Title formatting: Font size 16, bold font weight, and dark blue color for the title ("WHO Regions Distribution")
• Explode the EURO slice to emphasize it

Additional settings:

• Add a shadow effect
• Ensure the pie chart is a perfect circle by setting the axis to equal
• Use .tight_layout() for proper formatting and display the plot

In this task, you will create a histogram to visualize the distribution of cumulative deaths, with customized bin size and formatting. Follow the steps below:

Figure size : 12 by 7

Bins: Divide the data into 15 bins

Bar color: Red, with black outlines

Transparency: Set alpha to 0.7 for the bars

Title: "Distribution of Cumulative Deaths" (Font size: 18, Font weight: bold, Color: dark blue)

x-axis label: "Cumulative Deaths" (Font size: 14, Color: purple)

y-axis label: "Frequency" (Font size: 14, Color: green)

Grid: Add dashed lines (--) with linewidth=0.6 and alpha=0.8

Tick customization:

• x-axis ticks: Adjust label size to 12 and set label rotation to 45 degrees
• y-axis ticks: Set labelsize=12

Finally, use .tight_layout() to ensure proper formatting and display the plot.

In this activity, you'll use the Series daily_cases to create a pie chart, despite it being an inappropriate choice for this data type. The goal is to highlight the limitations of using a pie chart for time series data like daily COVID-19 cases. Use the following parameters to plot the chart:

• Figure size : 8 by 8

• Start angle: 90 degrees

• Percentage display (autopct): %1.1f%%

• Title: "New COVID-19 Cases Over Time"

Apply .tight_layout() and display the plot.

Using the region_cases Series, which records the number of new COVID-19 cases in each WHO (World Health Organization) region, construct a bar chart to present this data visually.

Figure size: 8 x 6

Title: New COVID-19 Cases by WHO Region

y-axis label: New Cases

Apply .tight_layout() and display the plot.

In this activity, you will generate a scatter plot to visualize the cumulative COVID-19 cases for countries within the EMRO region using the DataFrame case.

Figure size: 10 x 6

x-axis : Country, fontsize=12

y-axis : Cumulative Cases, fontsize=12

Color : green

Title: Cumulative Cases in EMRO (Incorrect - Scatter Plot), fontsize = 16

Rotate the x-axis labels by 45 degrees for better readability and use .tight_layout() to display the plot

In this activity, you will create a line plot to visualize both new COVID-19 cases and new deaths. You will work with the df_grouped DataFrame, which aggregates the daily new cases and deaths. . Follow the parameters outlined below:

Figure Size: 10 x 6

New cases

• Color: Blue
• Line Width: 2
• Line Label: "New Cases"
• x-axis Label: "Date Reported" (Font size: 12)
• y-axis Label: "New Cases" (Color: Blue, Font size: 12)
• x-ticks: Rotate by 45 degrees
• y-ticks: Set label color to Blue

New Deaths

• Color: Red
• Line Width: 2
• Line Label: "New Deaths"
• y-axis Label: "New Deaths" (Font Color: Red, Font Size: 12)
• y-ticks: Set label color to red.

Legend

• Location: Upper left
• Bounding Box Anchor: (0.1, 0.9) with bbox_transform=ax1.transAxes
• Font Size: 12

Title - Text: "New COVID-19 Cases and Deaths Over Time (7-Day Rolling Average)" - Font Size: 16

Finally, apply .tight_layout() and display the plot.

In this activity, you will create a visually engaging scatter plot to represent the trend of new COVID-19 cases over time, utilizing a colormap for better insight into the data. Use the scatter function to plot Date_reported on the x-axis and New_cases on the y-axis with the following parameters:

Figure size : 10 by 6

cmap : viridis

size : Set the size of the scatter points to 50.

alpha : 0.8 for slight transparency.

Colorbar Addition:

Include a colorbar to provide a reference for the color mapping, labeling it as "Number of New Cases" to clarify the representation of color intensity.

Title : "Scatter Plot of New COVID-19 Cases" with a font size of 16 to ensure visibility.

Label the x-axis as "Date Reported" and the y-axis as "New Cases," both with a font size of 12 for clarity.

Rotate the x-axis ticks by 45 degrees to improve readability of date labels, and use .tight_layout() to ensure all elements are displayed without overlap.