Analysis of Big Countries using Python

Analysis of Big Countries is a popular database question, which is now asked in Data Science interviews to test your data manipulation skills using SQL, Python, and R. So, if you want to learn how to solve this problem using Python, this article is for you. In this article, I’ll take you through solving the Analysis of Big Countries problem using Python.

Analysis of Big Countries: Problem Statement

You are given a sample dataset containing information on countries and their populations. The dataset is represented as a DataFrame in Pandas, named countries_df. Each row in the DataFrame represents a country, and the columns include the following information:

• country_id: A unique identifier for the country;
• name: The name of the country;
• area: The total area of the country in square kilometres;
• population: The total population of the country;
• continent: The continent where the country is located;

Here is a sample of the DataFrame countries_df:

And here’s the Python code to create this sample DataFrame:

data = {
    'country_id': [1, 2, 3],
    'name': ['CountryA', 'CountryB', 'CountryC'],
    'area': [755000, 431000, 293000],
    'population': [15000000, 25000000, 9000000],
    'continent': ['ContinentX', 'ContinentY', 'ContinentZ']
    }
countries_df = pd.DataFrame(data)

Your task is to use Pandas to identify “Big Countries”. A “Big Country” is defined as a country that either has an area greater than or equal to 500,000 square kilometres or has a population greater than or equal to 20 million people. Write a Python function that takes the DataFrame countries_df as input and returns a new DataFrame containing only the rows (countries) that meet the “Big Country” criteria. The resulting DataFrame should include all columns from the original DataFrame.

Requirements:

• The function should handle any DataFrame with the structure described above, not just the sample data.
• Ensure that the returned DataFrame is sorted by country_id to maintain a consistent order.
• Include appropriate error handling to manage possible exceptions, such as invalid input data types.

Example Output:

Given the sample data provided above, the output DataFrame would look like this:

Analysis of Big Countries using Python

To solve the Analysis of Big Countries problem using Python and Pandas, we can follow these steps:

1. We need to filter countries_df to find rows where either the area is greater than or equal to 500,000 square kilometres, or the population is greater than or equal to 20 million people.
2. Ensure the filtered DataFrame is sorted by country_id for consistent order.
3. Implement basic error handling to ensure the function deals gracefully with invalid inputs.

Here’s a Python function that implements these steps to solve the problem of Analysis of Big Countries:

import pandas as pd

def find_big_countries(countries_df):
    try:
        # ensure the input is a dataframe
        if not isinstance(countries_df, pd.DataFrame):
            raise ValueError("Input must be a pandas DataFrame.")

        # define the filtering condition for 'Big Countries'
        big_countries_condition = (countries_df['area'] >= 500000) | (countries_df['population'] >= 20000000)

        # filter the dataframe based on the condition
        big_countries_df = countries_df[big_countries_condition]

        # sort the filtered dataframe by 'country_id'
        big_countries_df_sorted = big_countries_df.sort_values(by='country_id')

        return big_countries_df_sorted
    except Exception as e:
        # handle potential errors
        print(f"An error occurred: {e}")
        return pd.DataFrame()  # return an empty dataframe in case of error

# driver code
if __name__ == "__main__":
    # sample data
    data = {
        'country_id': [1, 2, 3],
        'name': ['CountryA', 'CountryB', 'CountryC'],
        'area': [755000, 431000, 293000],
        'population': [15000000, 25000000, 9000000],
        'continent': ['ContinentX', 'ContinentY', 'ContinentZ']
    }
    countries_df = pd.DataFrame(data)

    # finding big countries
    big_countries_df = find_big_countries(countries_df)
    print(big_countries_df)

   country_id      name    area  population   continent
0           1  CountryA  755000    15000000  ContinentX
1           2  CountryB  431000    25000000  ContinentY

Our function starts by verifying that the input is a Pandas DataFrame. It then applies the filtering condition to identify “Big Countries” based on the defined criteria. After filtering, the results are sorted by country_id to ensure a consistent order. If any errors occur (e.g., the input is not a DataFrame), the function handles these gracefully by printing an error message and returning an empty DataFrame.

Summary

So, this is how you can solve the Analysis of Big Countries problem using Python. Analysis of Big Countries is a popular database question, which is now asked in Data Science interviews to test your data manipulation skills using SQL, Python, and R.

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