PANDAS

Pandas is a library of the Python programming language, entirely dedicated to Data Science. Find out what this tool is used for, and why it is essential for Data Scientists.

Created in 1991, Python is the most popular programming language for data analysis and Machine Learning. There are several advantages that explain its success with Data Scientists.

First of all, it is a very easy to use language. Even a beginner can quickly produce programs thanks to its simple and intuitive syntax.

This language federates a vast community, having created many tools for Data Science. There are for example tools for Data Visualization such as Seaborn and Matplotlib, and software libraries such as Numpy. One of these libraries is Pandas, designed for data manipulation and analysis.

What is Pandas?

The Pandas open-source software library is specifically designed for data manipulation and analysis in Python. It is powerful, flexible and easy to use.

Thanks to Pandas, the Python language can finally be used to load, align, manipulate or merge data. The performance is particularly impressive when the back-end source code is written in C or Python.

The name "Pandas" is actually a contraction of the term "Panel Data" for data sets that include observations over multiple time periods. This library was created as a high-level tool for analysis in Python.

The creators of Pandas plan to evolve this library to become the most powerful and flexible open-source data analysis and manipulation tool in any programming language.

In addition to data analysis, Pandas is widely used for data wrangling. This term encompasses methods for transforming unstructured data to make it usable.

In general, Pandas also excels at processing structured data in the form of tables, matrices or time series. It is also compatible with other Python libraries.