Task 4: COVID-19 EDA II

For this task, you’ll do some further analysis on the .csv file you made from Task 3.

Remember to run the following commands below to import all the necessary packages to run this Task.

import pandas as pd
import numpy as np
import matplotlib

4.1: Importing filtered data

First, import the datafiltered.csv file back into a dataframe and call the variable df.

# Your solution here

Then, remove the additional index column that was made when you exported the file.

Sample output

	date	new_cases	new_deaths	new_tests	incident_rate
0	2020-04-20	3047	433	7841	0.388598
1	2020-04-21	2256	454	28095	0.080299
2	2020-04-22	2729	534	44248	0.0616751
3	2020-04-23	3370	437	37083	0.0908772
4	2020-04-24	2646	464	95273	0.0277728
5	2020-04-25	3021	420	38676	0.0781105
6	2020-04-26	2357	415	24113	0.0977481
7	2020-04-27	2324	260	26678	0.087113
8	2020-04-28	1739	333	37554	0.0463067
9	2020-04-29	2091	382	38589	0.0541864

# Your solution here

Finally, print out 10 random rows in df to ensure your data has been imported correctly. (Hint: There is a pandas function that does this)

# Your solution here

4.2: Grouping data

If we want to know the average number of rates by the months in the year, we can use a groupby object to better understand how seasonal changes can affect COVID-19.

For this task, you should group the dataframe by month, then select the new_cases, new_deaths column, and then calculate the average value by month.

Make sure the output is displayed without using the print() function, remember that the last cell in a jupyter notebook will always be displayed.

Sample Output

date	new_cases	new_deaths
4	2515.09	405
5	937.839	182.516
6	259.067	46.8
7	216.839	12.5161
8	679.355	11.129
9	996	6

For this sub-task, a research question that this table would’ve answered might look something like this:

“What is the mean number of new cases and deaths by month?”

# Your solution here

4.3: Cumulative Sums

A cumulative sum is a partial sum of each row, growing in total as you go down the list.

Using the original dataframe, df, create two new columns titled cumulative_new_cases and cumulative_new_tests.

Make sure the output is displayed without using the print() function, remember that the last cell in a jupyter notebook will always be displayed.

Sample Output

	date	new_cases	new_deaths	new_tests	incident_rate	cumulative_new_cases	cumulative_new_tests
0	2020-04-20	3047	433	7841	0.388598	3047	7841
1	2020-04-21	2256	454	28095	0.080299	5303	35936
2	2020-04-22	2729	534	44248	0.0616751	8032	80184
3	2020-04-23	3370	437	37083	0.0908772	11402	117267
4	2020-04-24	2646	464	95273	0.0277728	14048	212540
5	2020-04-25	3021	420	38676	0.0781105	17069	251216
6	2020-04-26	2357	415	24113	0.0977481	19426	275329
7	2020-04-27	2324	260	26678	0.087113	21750	302007
8	2020-04-28	1739	333	37554	0.0463067	23489	339561
9	2020-04-29	2091	382	38589	0.0541864	25580	378150

For this sub-task, a research question that this table would’ve answered might look something like this:

“What is the sum as time progresses for the number of new cases and new tests?”

# Your solution here

4.4: Visualising Data

4.4.1: Set the Seaborn figure theme and scale up the text in the figures

There are five preset Seaborn styles (or themes): darkgrid, whitegrid, dark, white, and ticks. They are each suited to different applications and personal preferences. You can see what they look like here.

Hint: You will need to use the font_scale property of the set_theme() function in Seaborn.

Once you’ve done that, create the same plot as in 1.1 and confirm that it looks bigger. Once you are able to confirm this, you will see all subsequent plots in this Jupyter Notebook using the same theme.

Remember to copy this code above to your other Jupyter Notebooks as well!!

# Your solution here

4.4.2: Visualize the COVID-19 dataset

You’ve previously done work on the COVID-19 dataset, filtering through and analysing the data.

However, data is best represented visually (usually), so we should take our data and visually represent some points.

You’re task is to create a simple plot of the cumulative_new_cases column.

Sample Output

Note: We have left off the themes, axis-labels, axis titles, and plot titles so you can spend some time interpreting what you’re plotting. Make sure your plot has all the components of what makes an effective plot!

# Your solution here