Data analysis using Pandas and NumPy in Python

Pandas is an open-source package in Python that is widely used for data analysis and machine learning. Pandas is built on another package named Numpy which provides support for multi-dimensional arrays.

These libraries provide a really fast and efficient way to manage and explore data. They help us to create Series and DataFrames to efficiently represent data and manipulate it in various ways.

Pandas provide a wide array of built-in tools to read and write the data into data structures, web services, databases etc. Pandas support all kinds of files, whether it is CSV, JSON, Excel and HDF5.

Data is very vast and it can be confusing to read. Pandas library has integrated features to handle missing data values which makes data cleaner and simpler to read.

These Pandas features won’t make sense to beginners immediately, but they will be of great use in the future. As we go deeper into learning Pandas we will see how essential and useful these features are, for a data scientist.

In this article and the coming series, I am going to talk about how these libraries help in analysing data in finance and helps to create models for optimization and visualization.

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. I have fetched the data from the economist about the highest used coding languages in the United States.

As per this chart, Python has become the most widely used language in recent years.

Pandas with its DataFrame and Series objects, and Numpy with its ndarray are the workhorses of financial analysis with Python for finance professionals.

Combined with matplotlib and other visualization libraries, you have great tools at your disposal to assist productivity.

In the coming articles, I will explain simplified versions of creating Monte Carlo Simulations with Python.