Python provides excellent support for working with CSV, JSON, and XML data formats, making it a popular choice among data scientists. Each format has its own characteristics and use cases.

CSV (Comma-Separated Values) is a simple format for tabular data. Python’s built-in CSV module allows for easy reading and writing of CSV files. You can quickly convert data to and from CSV using libraries like Pandas, which simplifies the process significantly.

JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write. Python has a built-in JSON module that facilitates reading and writing JSON data. When you read a CSV file into Python, it can be converted into a dictionary, which can then be easily written to a JSON file.

XML (eXtensible Markup Language) is more complex than CSV and JSON but offers features like namespaces and schema definitions. Python’s built-in XML module, along with libraries like xmltodict, allows you to parse XML data and convert it into dictionaries, which can then be transformed into CSV or JSON formats as needed ¹²³⁴.