Python provides excellent support for working with CSV, JSON, and XML data formats, making it a popular choice among data scientists.

For CSV files, Python’s built-in csv module allows for easy reading and writing. You can set up your field names in a list and your data in a list of lists. Using the Pandas library simplifies this process even further, enabling you to read from and write to CSV files with just a single line of code. Pandas can also convert CSV data into a list of dictionaries, which can then be manipulated as needed ¹.

When it comes to JSON, Python has a built-in module that makes reading and writing JSON data straightforward. JSON maintains a dictionary-style structure, making it easy to work with. You can convert CSV data to JSON and vice versa using Pandas or the built-in JSON module ¹.

XML, while more complex, can also be handled in Python. You can use the xmltodict library to convert XML data into a Python dictionary, which can then be transformed into CSV or JSON formats. XML is heavier than CSV and JSON but offers features like namespaces and better representation for hierarchical data ¹²⁴.