1. pandas basics

In this assignment, we will practice some of the concepts and skills covered Section 06.

1.1 Download the demo file contains basic information of world countries, read it as dataframe countries_df.

1.2 How many countries does the dataframe contain?

[Hint: Use the .shape method]

1.3 Retrieve a list of continents from the dataframe?

[Hint: Use the .unique method of a series]

1.4 What is the total population of all the countries listed in this dataset?

1.5 What is the overall life expectancy across the world?

[Hint: You’ll need to take a weighted average of life expectancy using populations as weights]

1.6 Create a dataframe containing 10 countries with the highest population.

1.7 Add a new column in countries_df to record the overall GDP per country.

1.8 Create a dataframe containing 10 countries with the lowest GDP per capita, among the counties with a population greater than 100 million.

1.9 Create a dataframe that counts the number of countries in each continent?

1.10 Create a dataframe showing the total population of each continent.

2. COVID-19 stats

2.1 Let’s download another CSV file containing overall COVID-19 stats for various countries, and read the data into another pandas dataframe covid_data_df.

2.2 Count the number of countries for which the total_tests data is missing.

[Hint: Use the .isna method]

2.3 Let’s merge the two dataframes (countries_df and covid_data_df) on the location column, and name the merged dataframe combined_df

[Hint: Use the .merge method on countries_df. Search how to use .merge by yourself]

2.4 Add columns tests_per_million, cases_per_million and deaths_per_million into combined_df.

2.5 Create a dataframe with 10 counties that have the highest number of tests per million people.

2.6 Create a dataframe with 10 counties that have the highest number of cases per million people.

2.7 Count number of countries that feature in both the lists of “highest number of tests per million” (from 2.5) and “highest number of cases per million” (from 2.6).

[Hint: Use the .merge method again]

2.8 Count number of countries that feature in both the lists “20 countries with the lowest GDP per capita” and “20 countries with the lowest number of hospital beds per thousand population”. Only consider countries with a population higher than 10 million while creating the list.