How to Efficiently Extract City Data by Country in Python using Pandas

Learn how to filter and structure city data related to a specific country using Pandas in Python, enabling you to generate neatly organized outputs.
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This video is based on the question https://stackoverflow.com/q/70491581/ asked by the user 'Dinesh' ( https://stackoverflow.com/u/15806383/ ) and on the answer https://stackoverflow.com/a/70491758/ provided by the user 'Tamil Selvan' ( https://stackoverflow.com/u/10383650/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Efficiently Extracting City Data by Country in Python using Pandas

Data manipulation is a crucial skill when it comes to handling and analyzing information. In this guide, we will tackle a common scenario where users want to query city information based on selected countries using Python's powerful Pandas library. We’ll explain how to achieve the desired output format step-by-step.

The Problem

Imagine you have a dataset containing various countries and their corresponding cities. You want to filter and display cities based on a specific country selected by the user. However, you need the output in a certain format: a dictionary where the key is the country and the value is a list of cities belonging to that country.

Here’s an example of how the output should look:

[[See Video to Reveal this Text or Code Snippet]]

Setting Up the Data

First, we need to create a DataFrame that holds our country and city data. Here’s the initial setup we’ll work with:

[[See Video to Reveal this Text or Code Snippet]]

How the DataFrame Looks

The DataFrame will look like this:

[[See Video to Reveal this Text or Code Snippet]]

User Input

Next, we will prompt the user to enter a country. Upon receiving the input, we will convert it to uppercase to ensure consistency, as shown below:

[[See Video to Reveal this Text or Code Snippet]]

Filtering City Data

Now comes the core functionality—filter the DataFrame based on the selected country and convert the city names into a list. The code block to achieve this looks like this:

[[See Video to Reveal this Text or Code Snippet]]

Explanation

In this section:

We use the df['country'] == a condition to filter rows where the country matches the selected value.

The ['city'].to_list() method transforms the filtered cities into a list format.

Finally, we structure the output as a dictionary with the country name as the key.

Example Output

When a user selects "INDIA," the output generated will be:

[[See Video to Reveal this Text or Code Snippet]]

This is neatly organized and adheres to the required format.

Complete Example

Here’s the complete code combining all the parts discussed:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Using Pandas for data manipulation, particularly for filtering data based on user input, can greatly streamline your workflow. This method provides a clean, efficient way to produce outputs tailored to your needs. If you enjoy working with data or want to learn more about pandas, there's a wealth of resources available to help you along your journey.

Happy coding!

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