- It provides a really fast and efficient way to manage and explore data.
- It is easy to clean data missing values handling with help of pandas.
- There are methods of alignment and indexing which take proper care of organising and labelling data.
- It has wide range of built-in tools for purpose of reading and writing.
- It supports multiple files formats.
- It also provide merging and joining.
- It has in-built technology to plot graph of our data of various kinds and also do statistics and frequency conversion which is helpful for data scientists.
- It can group and separate data accordingly.
- It can also implement a mathematical operation on the data.
- Data representation: By using DataFrame and Series data structures we can easily represent data in normally suited for data analysis. If we do the same with any other programming language like JAVA, C, C++ would require many lines of code, as these languages were not built for data analysis.
- Data subsetting and filtering: It provides data filtering procedures that are a staple of doing data analysis.
- Concise and clear code: It's API allow the user to focus more on the core goal rather than writing a lot of scaffolding code in order to perform routine tasks.