Data manipulation Techniques with Pandas for FP&A and Finance

If you don’t what is Python and how you can leverage for Finance, read this first.

In a nutshell, Python is a popular programming language used in many industries, including finance. FP&A (Financial Planning & Analysis) professionals can benefit from learning Python as it can help them automate tasks, analyze data, and create visualizations.

As an FP&A professional, one of the most important tasks you face is working with large and complex data sets. This can be a time-consuming and challenging task, but with the help of the Python Pandas library, it can be much easier and more efficient.

In this article, we will explore some advanced data manipulation techniques with Pandas, including data filtering, aggregation, and transformation.

We will also provide Python code examples to illustrate each technique.

Data Filtering with Pandas

One of the most basic tasks in data analysis is filtering data. Pandas provides several ways to filter data, including boolean indexing, query method, and loc and iloc attributes.