What are some differences between Pandas, Numpy and Matplotlib?



What are some differences between the Python data science modules Pandas, Numpy and Matplotlib?


Although they may appear similar, these modules have unique purposes and functionalities.

The Pandas module is used for working with tabular data. It allows us to work with data in table form, such as in CSV or SQL database formats. We can also create tables of our own, and edit or add columns or rows to tables. Pandas provides us with some powerful objects like DataFrames and Series which are very useful for working with and analyzing data.

The Numpy module is mainly used for working with numerical data. It provides us with a powerful object known as an Array. With Arrays, we can perform mathematical operations on multiple values in the Arrays at the same time, and also perform operations between different Arrays, similar to matrix operations.

Last, but not least, the Matplotlib module is used for data visualization. It provides functionality for us to draw charts and graphs, so that we can better understand and present the data visually.

These modules have different purposes and functionality they excel at, and together they allow us to analyze, manipulate and visualize data in very useful ways.